{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Replication\log file.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}18 Dec 2019, 22:40:16

{com}. do "C:\Users\LUKASO~1\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. ***Reaching Out to the Voter? Campaigning on Twitter During the 2019 European Elections***
. 
. ***Table 1***
. use "C:\Replication\R&P replication data2.dta", clear
{txt}
{com}. xtmixed tweets_base citizens_rep pref_vote avg_dm pref_dm March_safety seatshare government ms_social_network lrgen_w_x galtan_w_x eu_position_w_x followers_base ep_seniority age female ep_leadership vchair epg_no2 epg_no3 epg_no4 epg_no5 epg_no6 epg_no7 epg_no8 epg_no9 || ms_code:, robust cluster(ms_code)
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-3354.8347}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-3354.6381}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-3354.6379}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}       314
{txt}Group variable: {res}ms_code{col 49}{txt}Number of groups{col 67}={col 69}{res}        27

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      11.6
{txt}{col 63}max{col 67}={col 69}{res}        42

{col 49}{txt}Wald chi2({res}25{txt}){col 67}={col 70}{res}  4468.95
{txt}Log pseudolikelihood = {res}-3354.6379{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 83:(Std. Err. adjusted for {res:27} clusters in ms_code)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}      tweets_base{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}citizens_rep {c |}{col 19}{res}{space 2} 133.1306{col 31}{space 2} 49.69131{col 42}{space 1}    2.68{col 51}{space 3}0.007{col 59}{space 4} 35.73738{col 72}{space 3} 230.5237
{txt}{space 8}pref_vote {c |}{col 19}{res}{space 2} 3759.781{col 31}{space 2} 3085.563{col 42}{space 1}    1.22{col 51}{space 3}0.223{col 59}{space 4}-2287.812{col 72}{space 3} 9807.373
{txt}{space 11}avg_dm {c |}{col 19}{res}{space 2} 19.91749{col 31}{space 2} 52.66731{col 42}{space 1}    0.38{col 51}{space 3}0.705{col 59}{space 4}-83.30854{col 72}{space 3} 123.1435
{txt}{space 10}pref_dm {c |}{col 19}{res}{space 2}-88.94289{col 31}{space 2} 117.5759{col 42}{space 1}   -0.76{col 51}{space 3}0.449{col 59}{space 4}-319.3874{col 72}{space 3} 141.5016
{txt}{space 5}March_safety {c |}{col 19}{res}{space 2}-294.8458{col 31}{space 2} 669.9172{col 42}{space 1}   -0.44{col 51}{space 3}0.660{col 59}{space 4}-1607.859{col 72}{space 3} 1018.168
{txt}{space 8}seatshare {c |}{col 19}{res}{space 2} 4709.559{col 31}{space 2} 6612.896{col 42}{space 1}    0.71{col 51}{space 3}0.476{col 59}{space 4} -8251.48{col 72}{space 3}  17670.6
{txt}{space 7}government {c |}{col 19}{res}{space 2}-1673.326{col 31}{space 2} 1599.739{col 42}{space 1}   -1.05{col 51}{space 3}0.296{col 59}{space 4}-4808.756{col 72}{space 3} 1462.105
{txt}ms_social_network {c |}{col 19}{res}{space 2} 280.8083{col 31}{space 2} 147.1941{col 42}{space 1}    1.91{col 51}{space 3}0.056{col 59}{space 4}-7.686942{col 72}{space 3} 569.3035
{txt}{space 8}lrgen_w_x {c |}{col 19}{res}{space 2}  17.0511{col 31}{space 2} 182.1371{col 42}{space 1}    0.09{col 51}{space 3}0.925{col 59}{space 4} -339.931{col 72}{space 3} 374.0332
{txt}{space 7}galtan_w_x {c |}{col 19}{res}{space 2} 59.35638{col 31}{space 2} 168.4889{col 42}{space 1}    0.35{col 51}{space 3}0.725{col 59}{space 4}-270.8758{col 72}{space 3} 389.5886
{txt}{space 2}eu_position_w_x {c |}{col 19}{res}{space 2} -110.251{col 31}{space 2} 223.6957{col 42}{space 1}   -0.49{col 51}{space 3}0.622{col 59}{space 4}-548.6864{col 72}{space 3} 328.1845
{txt}{space 3}followers_base {c |}{col 19}{res}{space 2} .0384575{col 31}{space 2} .0133259{col 42}{space 1}    2.89{col 51}{space 3}0.004{col 59}{space 4} .0123391{col 72}{space 3} .0645759
{txt}{space 5}ep_seniority {c |}{col 19}{res}{space 2}-.3410895{col 31}{space 2} .3944165{col 42}{space 1}   -0.86{col 51}{space 3}0.387{col 59}{space 4}-1.114132{col 72}{space 3} .4319527
{txt}{space 14}age {c |}{col 19}{res}{space 2}-48.67017{col 31}{space 2}  74.3612{col 42}{space 1}   -0.65{col 51}{space 3}0.513{col 59}{space 4}-194.4155{col 72}{space 3} 97.07511
{txt}{space 11}female {c |}{col 19}{res}{space 2}-1143.891{col 31}{space 2} 1061.493{col 42}{space 1}   -1.08{col 51}{space 3}0.281{col 59}{space 4}-3224.379{col 72}{space 3} 936.5961
{txt}{space 4}ep_leadership {c |}{col 19}{res}{space 2} -206.846{col 31}{space 2} 1974.092{col 42}{space 1}   -0.10{col 51}{space 3}0.917{col 59}{space 4}-4075.995{col 72}{space 3} 3662.303
{txt}{space 11}vchair {c |}{col 19}{res}{space 2}-1329.828{col 31}{space 2} 1070.448{col 42}{space 1}   -1.24{col 51}{space 3}0.214{col 59}{space 4}-3427.868{col 72}{space 3}  768.212
{txt}{space 10}epg_no2 {c |}{col 19}{res}{space 2}-2272.389{col 31}{space 2} 3080.193{col 42}{space 1}   -0.74{col 51}{space 3}0.461{col 59}{space 4}-8309.456{col 72}{space 3} 3764.678
{txt}{space 10}epg_no3 {c |}{col 19}{res}{space 2}-608.8961{col 31}{space 2}  2653.84{col 42}{space 1}   -0.23{col 51}{space 3}0.819{col 59}{space 4}-5810.328{col 72}{space 3} 4592.536
{txt}{space 10}epg_no4 {c |}{col 19}{res}{space 2}-651.9635{col 31}{space 2} 1790.894{col 42}{space 1}   -0.36{col 51}{space 3}0.716{col 59}{space 4}-4162.052{col 72}{space 3} 2858.124
{txt}{space 10}epg_no5 {c |}{col 19}{res}{space 2} 4909.046{col 31}{space 2} 5423.783{col 42}{space 1}    0.91{col 51}{space 3}0.365{col 59}{space 4}-5721.373{col 72}{space 3} 15539.47
{txt}{space 10}epg_no6 {c |}{col 19}{res}{space 2} 1844.398{col 31}{space 2} 2194.746{col 42}{space 1}    0.84{col 51}{space 3}0.401{col 59}{space 4}-2457.225{col 72}{space 3} 6146.021
{txt}{space 10}epg_no7 {c |}{col 19}{res}{space 2} 9812.963{col 31}{space 2} 2845.423{col 42}{space 1}    3.45{col 51}{space 3}0.001{col 59}{space 4} 4236.036{col 72}{space 3} 15389.89
{txt}{space 10}epg_no8 {c |}{col 19}{res}{space 2} 876.5003{col 31}{space 2} 2577.821{col 42}{space 1}    0.34{col 51}{space 3}0.734{col 59}{space 4}-4175.936{col 72}{space 3} 5928.936
{txt}{space 10}epg_no9 {c |}{col 19}{res}{space 2}-5602.927{col 31}{space 2} 3702.092{col 42}{space 1}   -1.51{col 51}{space 3}0.130{col 59}{space 4}-12858.89{col 72}{space 3} 1653.041
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} -18827.3{col 31}{space 2} 11183.91{col 42}{space 1}   -1.68{col 51}{space 3}0.092{col 59}{space 4}-40747.35{col 72}{space 3} 3092.756
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}ms_code{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33}  737.313{col 44} 4225.928{col 58} .0097492{col 70} 5.58e+07
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 10533.27{col 44} 2816.616{col 58} 6236.637{col 70} 17790.01
{txt}{hline 29}{c BT}{hline 48}

{com}. outreg2 using twitter3_1.xls, label ctitle("Model I","base") onecol dec(3) replace
{txt}{browse `"twitter3_1.xls"'}
{browse `"C:\Replication"' :dir}{com} : {txt}{stata `"seeout using "twitter3_1.txt", label"':seeout}

{com}. xtmixed tminus2m citizens_rep pref_vote avg_dm pref_dm March_safety seatshare government ms_social_network lrgen_w_x galtan_w_x eu_position_w_x followers_tminus2m ep_seniority age female ep_leadership vchair epg_no2 epg_no3 epg_no4 epg_no5 epg_no6 epg_no7 epg_no8 epg_no9 || ms_code:, robust cluster(ms_code)
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2036.8368}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-2036.8368}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}       314
{txt}Group variable: {res}ms_code{col 49}{txt}Number of groups{col 67}={col 69}{res}        27

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      11.6
{txt}{col 63}max{col 67}={col 69}{res}        42

{col 49}{txt}Wald chi2({res}25{txt}){col 67}={col 70}{res}  1602.53
{txt}Log pseudolikelihood = {res}-2036.8368{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 84:(Std. Err. adjusted for {res:27} clusters in ms_code)}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}          tminus2m{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}citizens_rep {c |}{col 20}{res}{space 2} 1.795569{col 32}{space 2} 1.324899{col 43}{space 1}    1.36{col 52}{space 3}0.175{col 60}{space 4}-.8011848{col 73}{space 3} 4.392323
{txt}{space 9}pref_vote {c |}{col 20}{res}{space 2} 96.54763{col 32}{space 2} 60.34333{col 43}{space 1}    1.60{col 52}{space 3}0.110{col 60}{space 4}-21.72312{col 73}{space 3} 214.8184
{txt}{space 12}avg_dm {c |}{col 20}{res}{space 2}  .109746{col 32}{space 2}   1.1462{col 43}{space 1}    0.10{col 52}{space 3}0.924{col 60}{space 4}-2.136765{col 73}{space 3} 2.356257
{txt}{space 11}pref_dm {c |}{col 20}{res}{space 2}-4.028232{col 32}{space 2}  2.37458{col 43}{space 1}   -1.70{col 52}{space 3}0.090{col 60}{space 4}-8.682324{col 73}{space 3} .6258591
{txt}{space 6}March_safety {c |}{col 20}{res}{space 2}-17.57904{col 32}{space 2} 12.02547{col 43}{space 1}   -1.46{col 52}{space 3}0.144{col 60}{space 4}-41.14853{col 73}{space 3} 5.990458
{txt}{space 9}seatshare {c |}{col 20}{res}{space 2} 29.79207{col 32}{space 2} 98.49523{col 43}{space 1}    0.30{col 52}{space 3}0.762{col 60}{space 4} -163.255{col 73}{space 3} 222.8392
{txt}{space 8}government {c |}{col 20}{res}{space 2}-22.46648{col 32}{space 2} 17.21225{col 43}{space 1}   -1.31{col 52}{space 3}0.192{col 60}{space 4}-56.20187{col 73}{space 3} 11.26891
{txt}{space 1}ms_social_network {c |}{col 20}{res}{space 2}-.2683735{col 32}{space 2} 1.859479{col 43}{space 1}   -0.14{col 52}{space 3}0.885{col 60}{space 4}-3.912886{col 73}{space 3} 3.376139
{txt}{space 9}lrgen_w_x {c |}{col 20}{res}{space 2} 1.388722{col 32}{space 2} 2.563418{col 43}{space 1}    0.54{col 52}{space 3}0.588{col 60}{space 4}-3.635485{col 73}{space 3} 6.412929
{txt}{space 8}galtan_w_x {c |}{col 20}{res}{space 2}-1.523843{col 32}{space 2} 1.790054{col 43}{space 1}   -0.85{col 52}{space 3}0.395{col 60}{space 4}-5.032285{col 73}{space 3} 1.984599
{txt}{space 3}eu_position_w_x {c |}{col 20}{res}{space 2}-2.458532{col 32}{space 2} 2.479807{col 43}{space 1}   -0.99{col 52}{space 3}0.321{col 60}{space 4}-7.318864{col 73}{space 3} 2.401801
{txt}followers_tminus2m {c |}{col 20}{res}{space 2} .0004494{col 32}{space 2} .0001867{col 43}{space 1}    2.41{col 52}{space 3}0.016{col 60}{space 4} .0000834{col 73}{space 3} .0008154
{txt}{space 6}ep_seniority {c |}{col 20}{res}{space 2}-.0096257{col 32}{space 2} .0045322{col 43}{space 1}   -2.12{col 52}{space 3}0.034{col 60}{space 4}-.0185085{col 73}{space 3}-.0007428
{txt}{space 15}age {c |}{col 20}{res}{space 2}  .108941{col 32}{space 2} .7361188{col 43}{space 1}    0.15{col 52}{space 3}0.882{col 60}{space 4}-1.333825{col 73}{space 3} 1.551707
{txt}{space 12}female {c |}{col 20}{res}{space 2} 5.556517{col 32}{space 2} 16.57156{col 43}{space 1}    0.34{col 52}{space 3}0.737{col 60}{space 4}-26.92314{col 73}{space 3} 38.03617
{txt}{space 5}ep_leadership {c |}{col 20}{res}{space 2} 48.42864{col 32}{space 2} 37.60992{col 43}{space 1}    1.29{col 52}{space 3}0.198{col 60}{space 4}-25.28545{col 73}{space 3} 122.1427
{txt}{space 12}vchair {c |}{col 20}{res}{space 2}-15.05099{col 32}{space 2}  14.8839{col 43}{space 1}   -1.01{col 52}{space 3}0.312{col 60}{space 4} -44.2229{col 73}{space 3} 14.12092
{txt}{space 11}epg_no2 {c |}{col 20}{res}{space 2} 18.39703{col 32}{space 2} 56.32725{col 43}{space 1}    0.33{col 52}{space 3}0.744{col 60}{space 4}-92.00235{col 73}{space 3} 128.7964
{txt}{space 11}epg_no3 {c |}{col 20}{res}{space 2}-4.048738{col 32}{space 2}  37.5436{col 43}{space 1}   -0.11{col 52}{space 3}0.914{col 60}{space 4}-77.63285{col 73}{space 3} 69.53537
{txt}{space 11}epg_no4 {c |}{col 20}{res}{space 2}  -9.2789{col 32}{space 2} 39.25134{col 43}{space 1}   -0.24{col 52}{space 3}0.813{col 60}{space 4}-86.21012{col 73}{space 3} 67.65232
{txt}{space 11}epg_no5 {c |}{col 20}{res}{space 2} 52.77104{col 32}{space 2} 53.55457{col 43}{space 1}    0.99{col 52}{space 3}0.324{col 60}{space 4}-52.19399{col 73}{space 3} 157.7361
{txt}{space 11}epg_no6 {c |}{col 20}{res}{space 2} 34.98699{col 32}{space 2} 36.51812{col 43}{space 1}    0.96{col 52}{space 3}0.338{col 60}{space 4}-36.58721{col 73}{space 3} 106.5612
{txt}{space 11}epg_no7 {c |}{col 20}{res}{space 2} 166.4387{col 32}{space 2} 69.03518{col 43}{space 1}    2.41{col 52}{space 3}0.016{col 60}{space 4} 31.13226{col 73}{space 3} 301.7452
{txt}{space 11}epg_no8 {c |}{col 20}{res}{space 2} 29.71736{col 32}{space 2} 43.86548{col 43}{space 1}    0.68{col 52}{space 3}0.498{col 60}{space 4} -56.2574{col 73}{space 3} 115.6921
{txt}{space 11}epg_no9 {c |}{col 20}{res}{space 2}-36.07816{col 32}{space 2} 56.49858{col 43}{space 1}   -0.64{col 52}{space 3}0.523{col 60}{space 4}-146.8133{col 73}{space 3} 74.65702
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} 2.158923{col 32}{space 2} 155.6887{col 43}{space 1}    0.01{col 52}{space 3}0.989{col 60}{space 4}-302.9854{col 73}{space 3} 307.3032
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}ms_code{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33}  43.4356{col 44} 10.97264{col 58} 26.47381{col 70} 71.26483
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 154.9953{col 44} 38.25552{col 58} 95.54924{col 70} 251.4258
{txt}{hline 29}{c BT}{hline 48}

{com}. outreg2 using twitter3_1.xls, append label ctitle("Model II","T-2m") onecol dec(3) 
{txt}{browse `"twitter3_1.xls"'}
{browse `"C:\Replication"' :dir}{com} : {txt}{stata `"seeout using "twitter3_1.txt", label"':seeout}

{com}. xtmixed tminus1m citizens_rep pref_vote avg_dm pref_dm April_safety seatshare government ms_social_network lrgen_w_x galtan_w_x eu_position_w_x followers_tminus1m ep_seniority age female ep_leadership vchair epg_no2 epg_no3 epg_no4 epg_no5 epg_no6 epg_no7 epg_no8 epg_no9 || ms_code:, robust cluster(ms_code)
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2092.2575}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-2092.2575}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}       314
{txt}Group variable: {res}ms_code{col 49}{txt}Number of groups{col 67}={col 69}{res}        27

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      11.6
{txt}{col 63}max{col 67}={col 69}{res}        42

{col 49}{txt}Wald chi2({res}25{txt}){col 67}={col 70}{res} 12054.35
{txt}Log pseudolikelihood = {res}-2092.2575{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 84:(Std. Err. adjusted for {res:27} clusters in ms_code)}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}          tminus1m{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}citizens_rep {c |}{col 20}{res}{space 2} 2.172181{col 32}{space 2} 1.473337{col 43}{space 1}    1.47{col 52}{space 3}0.140{col 60}{space 4}-.7155074{col 73}{space 3} 5.059869
{txt}{space 9}pref_vote {c |}{col 20}{res}{space 2} 71.72777{col 32}{space 2} 85.73465{col 43}{space 1}    0.84{col 52}{space 3}0.403{col 60}{space 4}-96.30906{col 73}{space 3} 239.7646
{txt}{space 12}avg_dm {c |}{col 20}{res}{space 2} .0793623{col 32}{space 2} 1.429106{col 43}{space 1}    0.06{col 52}{space 3}0.956{col 60}{space 4}-2.721633{col 73}{space 3} 2.880358
{txt}{space 11}pref_dm {c |}{col 20}{res}{space 2}-4.093244{col 32}{space 2}  3.24638{col 43}{space 1}   -1.26{col 52}{space 3}0.207{col 60}{space 4}-10.45603{col 73}{space 3} 2.269544
{txt}{space 6}April_safety {c |}{col 20}{res}{space 2}-22.18444{col 32}{space 2} 13.19458{col 43}{space 1}   -1.68{col 52}{space 3}0.093{col 60}{space 4}-48.04534{col 73}{space 3} 3.676461
{txt}{space 9}seatshare {c |}{col 20}{res}{space 2}-44.07386{col 32}{space 2} 106.1457{col 43}{space 1}   -0.42{col 52}{space 3}0.678{col 60}{space 4}-252.1157{col 73}{space 3} 163.9679
{txt}{space 8}government {c |}{col 20}{res}{space 2}-16.02231{col 32}{space 2} 24.86759{col 43}{space 1}   -0.64{col 52}{space 3}0.519{col 60}{space 4} -64.7619{col 73}{space 3} 32.71728
{txt}{space 1}ms_social_network {c |}{col 20}{res}{space 2} 1.201014{col 32}{space 2} 2.401842{col 43}{space 1}    0.50{col 52}{space 3}0.617{col 60}{space 4}-3.506511{col 73}{space 3} 5.908538
{txt}{space 9}lrgen_w_x {c |}{col 20}{res}{space 2} 3.745728{col 32}{space 2} 3.503552{col 43}{space 1}    1.07{col 52}{space 3}0.285{col 60}{space 4}-3.121108{col 73}{space 3} 10.61256
{txt}{space 8}galtan_w_x {c |}{col 20}{res}{space 2}-2.027003{col 32}{space 2} 2.808162{col 43}{space 1}   -0.72{col 52}{space 3}0.470{col 60}{space 4}-7.530898{col 73}{space 3} 3.476893
{txt}{space 3}eu_position_w_x {c |}{col 20}{res}{space 2}-5.929367{col 32}{space 2} 3.628933{col 43}{space 1}   -1.63{col 52}{space 3}0.102{col 60}{space 4}-13.04195{col 73}{space 3} 1.183212
{txt}followers_tminus1m {c |}{col 20}{res}{space 2}  .000531{col 32}{space 2} .0002649{col 43}{space 1}    2.00{col 52}{space 3}0.045{col 60}{space 4} .0000117{col 73}{space 3} .0010502
{txt}{space 6}ep_seniority {c |}{col 20}{res}{space 2}-.0089842{col 32}{space 2} .0056097{col 43}{space 1}   -1.60{col 52}{space 3}0.109{col 60}{space 4}-.0199791{col 73}{space 3} .0020107
{txt}{space 15}age {c |}{col 20}{res}{space 2}-.0726333{col 32}{space 2} .7954585{col 43}{space 1}   -0.09{col 52}{space 3}0.927{col 60}{space 4}-1.631703{col 73}{space 3} 1.486437
{txt}{space 12}female {c |}{col 20}{res}{space 2} 12.90375{col 32}{space 2} 24.24117{col 43}{space 1}    0.53{col 52}{space 3}0.595{col 60}{space 4}-34.60806{col 73}{space 3} 60.41556
{txt}{space 5}ep_leadership {c |}{col 20}{res}{space 2} 77.32614{col 32}{space 2} 51.56496{col 43}{space 1}    1.50{col 52}{space 3}0.134{col 60}{space 4}-23.73933{col 73}{space 3} 178.3916
{txt}{space 12}vchair {c |}{col 20}{res}{space 2}-11.95469{col 32}{space 2} 18.04678{col 43}{space 1}   -0.66{col 52}{space 3}0.508{col 60}{space 4}-47.32573{col 73}{space 3} 23.41635
{txt}{space 11}epg_no2 {c |}{col 20}{res}{space 2} 69.19454{col 32}{space 2} 79.06809{col 43}{space 1}    0.88{col 52}{space 3}0.382{col 60}{space 4}-85.77607{col 73}{space 3} 224.1652
{txt}{space 11}epg_no3 {c |}{col 20}{res}{space 2}-10.68301{col 32}{space 2} 70.64041{col 43}{space 1}   -0.15{col 52}{space 3}0.880{col 60}{space 4}-149.1357{col 73}{space 3} 127.7697
{txt}{space 11}epg_no4 {c |}{col 20}{res}{space 2}  14.7402{col 32}{space 2} 55.47374{col 43}{space 1}    0.27{col 52}{space 3}0.790{col 60}{space 4}-93.98633{col 73}{space 3} 123.4667
{txt}{space 11}epg_no5 {c |}{col 20}{res}{space 2} 93.37277{col 32}{space 2} 79.32198{col 43}{space 1}    1.18{col 52}{space 3}0.239{col 60}{space 4}-62.09545{col 73}{space 3}  248.841
{txt}{space 11}epg_no6 {c |}{col 20}{res}{space 2} 53.91743{col 32}{space 2} 54.02947{col 43}{space 1}    1.00{col 52}{space 3}0.318{col 60}{space 4}-51.97838{col 73}{space 3} 159.8132
{txt}{space 11}epg_no7 {c |}{col 20}{res}{space 2} 265.3798{col 32}{space 2} 101.1477{col 43}{space 1}    2.62{col 52}{space 3}0.009{col 60}{space 4} 67.13403{col 73}{space 3} 463.6256
{txt}{space 11}epg_no8 {c |}{col 20}{res}{space 2} 72.16199{col 32}{space 2} 61.91741{col 43}{space 1}    1.17{col 52}{space 3}0.244{col 60}{space 4}-49.19391{col 73}{space 3} 193.5179
{txt}{space 11}epg_no9 {c |}{col 20}{res}{space 2}-56.22793{col 32}{space 2} 78.91816{col 43}{space 1}   -0.71{col 52}{space 3}0.476{col 60}{space 4}-210.9047{col 73}{space 3} 98.44881
{txt}{space 13}_cons {c |}{col 20}{res}{space 2}-55.06316{col 32}{space 2} 205.6218{col 43}{space 1}   -0.27{col 52}{space 3}0.789{col 60}{space 4}-458.0745{col 73}{space 3} 347.9482
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}ms_code{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 62.75741{col 44} 13.59752{col 58} 41.04279{col 70} 95.96065
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 183.5335{col 44} 27.41408{col 58} 136.9534{col 70} 245.9563
{txt}{hline 29}{c BT}{hline 48}

{com}. outreg2 using twitter3_1.xls, append label ctitle("Model III","T-1m") onecol dec(3) 
{txt}{browse `"twitter3_1.xls"'}
{browse `"C:\Replication"' :dir}{com} : {txt}{stata `"seeout using "twitter3_1.txt", label"':seeout}

{com}. xtmixed tminus1w_combined citizens_rep pref_vote avg_dm pref_dm May_safety seatshare government ms_social_network lrgen_w_x galtan_w_x eu_position_w_x followers_tminus1w_com ep_seniority age female ep_leadership vchair epg_no2 epg_no3 epg_no4 epg_no5 epg_no6 epg_no7 epg_no8 epg_no9 || ms_code:, robust cluster(ms_code)
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2084.6121}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-2084.6121}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}       351
{txt}Group variable: {res}ms_code{col 49}{txt}Number of groups{col 67}={col 69}{res}        28

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      12.5
{txt}{col 63}max{col 67}={col 69}{res}        42

{col 49}{txt}Wald chi2({res}25{txt}){col 67}={col 70}{res}  1532.87
{txt}Log pseudolikelihood = {res}-2084.6121{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 88:(Std. Err. adjusted for {res:28} clusters in ms_code)}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}     tminus1w_combined{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}citizens_rep {c |}{col 24}{res}{space 2} 1.318242{col 36}{space 2}  .643663{col 47}{space 1}    2.05{col 56}{space 3}0.041{col 64}{space 4} .0566855{col 77}{space 3} 2.579798
{txt}{space 13}pref_vote {c |}{col 24}{res}{space 2}-7.592124{col 36}{space 2} 40.25207{col 47}{space 1}   -0.19{col 56}{space 3}0.850{col 64}{space 4}-86.48473{col 77}{space 3} 71.30048
{txt}{space 16}avg_dm {c |}{col 24}{res}{space 2}-.8497984{col 36}{space 2} .5435341{col 47}{space 1}   -1.56{col 56}{space 3}0.118{col 64}{space 4}-1.915106{col 77}{space 3} .2155089
{txt}{space 15}pref_dm {c |}{col 24}{res}{space 2}-.9947809{col 36}{space 2} 1.552526{col 47}{space 1}   -0.64{col 56}{space 3}0.522{col 64}{space 4}-4.037675{col 77}{space 3} 2.048114
{txt}{space 12}May_safety {c |}{col 24}{res}{space 2}-7.294881{col 36}{space 2} 5.125423{col 47}{space 1}   -1.42{col 56}{space 3}0.155{col 64}{space 4}-17.34052{col 77}{space 3} 2.750763
{txt}{space 13}seatshare {c |}{col 24}{res}{space 2}-10.61426{col 36}{space 2} 42.96644{col 47}{space 1}   -0.25{col 56}{space 3}0.805{col 64}{space 4}-94.82693{col 77}{space 3} 73.59841
{txt}{space 12}government {c |}{col 24}{res}{space 2}-18.90804{col 36}{space 2} 12.20819{col 47}{space 1}   -1.55{col 56}{space 3}0.121{col 64}{space 4}-42.83566{col 77}{space 3} 5.019578
{txt}{space 5}ms_social_network {c |}{col 24}{res}{space 2} .6945467{col 36}{space 2} 1.188998{col 47}{space 1}    0.58{col 56}{space 3}0.559{col 64}{space 4}-1.635847{col 77}{space 3}  3.02494
{txt}{space 13}lrgen_w_x {c |}{col 24}{res}{space 2} 2.892812{col 36}{space 2} 1.512414{col 47}{space 1}    1.91{col 56}{space 3}0.056{col 64}{space 4}-.0714647{col 77}{space 3} 5.857089
{txt}{space 12}galtan_w_x {c |}{col 24}{res}{space 2} .2922758{col 36}{space 2} 1.389732{col 47}{space 1}    0.21{col 56}{space 3}0.833{col 64}{space 4}-2.431549{col 77}{space 3}   3.0161
{txt}{space 7}eu_position_w_x {c |}{col 24}{res}{space 2}-2.551331{col 36}{space 2} 1.859882{col 47}{space 1}   -1.37{col 56}{space 3}0.170{col 64}{space 4}-6.196632{col 77}{space 3}  1.09397
{txt}followers_tminus1w_com {c |}{col 24}{res}{space 2} 6.39e-06{col 36}{space 2} .0000726{col 47}{space 1}    0.09{col 56}{space 3}0.930{col 64}{space 4}-.0001359{col 77}{space 3} .0001486
{txt}{space 10}ep_seniority {c |}{col 24}{res}{space 2}-.0055074{col 36}{space 2} .0018381{col 47}{space 1}   -3.00{col 56}{space 3}0.003{col 64}{space 4}-.0091101{col 77}{space 3}-.0019048
{txt}{space 19}age {c |}{col 24}{res}{space 2}-.0938902{col 36}{space 2} .3546361{col 47}{space 1}   -0.26{col 56}{space 3}0.791{col 64}{space 4}-.7889641{col 77}{space 3} .6011838
{txt}{space 16}female {c |}{col 24}{res}{space 2} 9.058423{col 36}{space 2} 9.902359{col 47}{space 1}    0.91{col 56}{space 3}0.360{col 64}{space 4}-10.34984{col 77}{space 3} 28.46669
{txt}{space 9}ep_leadership {c |}{col 24}{res}{space 2} 29.93983{col 36}{space 2} 21.72245{col 47}{space 1}    1.38{col 56}{space 3}0.168{col 64}{space 4}-12.63539{col 77}{space 3} 72.51506
{txt}{space 16}vchair {c |}{col 24}{res}{space 2}-11.70737{col 36}{space 2} 9.153401{col 47}{space 1}   -1.28{col 56}{space 3}0.201{col 64}{space 4}-29.64771{col 77}{space 3} 6.232962
{txt}{space 15}epg_no2 {c |}{col 24}{res}{space 2} 39.41324{col 36}{space 2} 26.03408{col 47}{space 1}    1.51{col 56}{space 3}0.130{col 64}{space 4}-11.61262{col 77}{space 3} 90.43911
{txt}{space 15}epg_no3 {c |}{col 24}{res}{space 2}-28.25896{col 36}{space 2}  38.2191{col 47}{space 1}   -0.74{col 56}{space 3}0.460{col 64}{space 4} -103.167{col 77}{space 3} 46.64911
{txt}{space 15}epg_no4 {c |}{col 24}{res}{space 2}-20.10088{col 36}{space 2} 27.62364{col 47}{space 1}   -0.73{col 56}{space 3}0.467{col 64}{space 4}-74.24222{col 77}{space 3} 34.04046
{txt}{space 15}epg_no5 {c |}{col 24}{res}{space 2} 63.44764{col 36}{space 2} 32.69985{col 47}{space 1}    1.94{col 56}{space 3}0.052{col 64}{space 4}-.6428913{col 77}{space 3} 127.5382
{txt}{space 15}epg_no6 {c |}{col 24}{res}{space 2} 37.36906{col 36}{space 2} 21.46504{col 47}{space 1}    1.74{col 56}{space 3}0.082{col 64}{space 4}-4.701654{col 77}{space 3} 79.43978
{txt}{space 15}epg_no7 {c |}{col 24}{res}{space 2} 144.8232{col 36}{space 2} 36.39209{col 47}{space 1}    3.98{col 56}{space 3}0.000{col 64}{space 4} 73.49599{col 77}{space 3} 216.1503
{txt}{space 15}epg_no8 {c |}{col 24}{res}{space 2} 52.24823{col 36}{space 2} 25.51584{col 47}{space 1}    2.05{col 56}{space 3}0.041{col 64}{space 4} 2.238097{col 77}{space 3} 102.2584
{txt}{space 15}epg_no9 {c |}{col 24}{res}{space 2}-63.74652{col 36}{space 2} 54.10638{col 47}{space 1}   -1.18{col 56}{space 3}0.239{col 64}{space 4}-169.7931{col 77}{space 3} 42.30004
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}-31.42353{col 36}{space 2} 101.4907{col 47}{space 1}   -0.31{col 56}{space 3}0.757{col 64}{space 4}-230.3417{col 77}{space 3} 167.4946
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}ms_code{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 26.27413{col 44} 7.644033{col 58} 14.85542{col 70} 46.46989
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 89.55199{col 44} 12.31544{col 58} 68.39362{col 70}  117.256
{txt}{hline 29}{c BT}{hline 48}

{com}. outreg2 using twitter3_1.xls, append label ctitle("Model IV","T-1w") onecol dec(3) 
{txt}{browse `"twitter3_1.xls"'}
{browse `"C:\Replication"' :dir}{com} : {txt}{stata `"seeout using "twitter3_1.txt", label"':seeout}

{com}. xtmixed tplus1m citizens_rep pref_vote avg_dm pref_dm May_safety seatshare government ms_social_network lrgen_w_x galtan_w_x eu_position_w_x followers_tplus1m ep_seniority age female ep_leadership vchair epg_no2 epg_no3 epg_no4 epg_no5 epg_no6 epg_no7 epg_no8 epg_no9 || ms_code:, robust cluster(ms_code)
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2132.5997}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-2132.5959}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-2132.5959}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}       351
{txt}Group variable: {res}ms_code{col 49}{txt}Number of groups{col 67}={col 69}{res}        28

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      12.5
{txt}{col 63}max{col 67}={col 69}{res}        42

{col 49}{txt}Wald chi2({res}25{txt}){col 67}={col 70}{res}  3476.83
{txt}Log pseudolikelihood = {res}-2132.5959{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 83:(Std. Err. adjusted for {res:28} clusters in ms_code)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}          tplus1m{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}citizens_rep {c |}{col 19}{res}{space 2} 1.017009{col 31}{space 2} .5951383{col 42}{space 1}    1.71{col 51}{space 3}0.087{col 59}{space 4} -.149441{col 72}{space 3} 2.183458
{txt}{space 8}pref_vote {c |}{col 19}{res}{space 2} 7.855401{col 31}{space 2} 39.27879{col 42}{space 1}    0.20{col 51}{space 3}0.841{col 59}{space 4}-69.12961{col 72}{space 3} 84.84041
{txt}{space 11}avg_dm {c |}{col 19}{res}{space 2}-.8842509{col 31}{space 2} .5215875{col 42}{space 1}   -1.70{col 51}{space 3}0.090{col 59}{space 4}-1.906544{col 72}{space 3} .1380418
{txt}{space 10}pref_dm {c |}{col 19}{res}{space 2}-1.486699{col 31}{space 2} 1.616173{col 42}{space 1}   -0.92{col 51}{space 3}0.358{col 59}{space 4} -4.65434{col 72}{space 3} 1.680943
{txt}{space 7}May_safety {c |}{col 19}{res}{space 2}-5.449555{col 31}{space 2} 6.381175{col 42}{space 1}   -0.85{col 51}{space 3}0.393{col 59}{space 4}-17.95643{col 72}{space 3} 7.057318
{txt}{space 8}seatshare {c |}{col 19}{res}{space 2} 24.78004{col 31}{space 2} 49.16903{col 42}{space 1}    0.50{col 51}{space 3}0.614{col 59}{space 4}-71.58949{col 72}{space 3} 121.1496
{txt}{space 7}government {c |}{col 19}{res}{space 2}-12.15681{col 31}{space 2} 12.94305{col 42}{space 1}   -0.94{col 51}{space 3}0.348{col 59}{space 4}-37.52471{col 72}{space 3}  13.2111
{txt}ms_social_network {c |}{col 19}{res}{space 2}-.8896327{col 31}{space 2} 1.322804{col 42}{space 1}   -0.67{col 51}{space 3}0.501{col 59}{space 4}-3.482281{col 72}{space 3} 1.703015
{txt}{space 8}lrgen_w_x {c |}{col 19}{res}{space 2} 1.760373{col 31}{space 2}  1.75229{col 42}{space 1}    1.00{col 51}{space 3}0.315{col 59}{space 4}-1.674053{col 72}{space 3} 5.194799
{txt}{space 7}galtan_w_x {c |}{col 19}{res}{space 2}-.4627085{col 31}{space 2} 1.615942{col 42}{space 1}   -0.29{col 51}{space 3}0.775{col 59}{space 4}-3.629896{col 72}{space 3} 2.704479
{txt}{space 2}eu_position_w_x {c |}{col 19}{res}{space 2}-3.485425{col 31}{space 2}  2.13928{col 42}{space 1}   -1.63{col 51}{space 3}0.103{col 59}{space 4}-7.678337{col 72}{space 3} .7074863
{txt}followers_tplus1m {c |}{col 19}{res}{space 2} .0000802{col 31}{space 2} .0000625{col 42}{space 1}    1.28{col 51}{space 3}0.199{col 59}{space 4}-.0000422{col 72}{space 3} .0002027
{txt}{space 5}ep_seniority {c |}{col 19}{res}{space 2}-.0084109{col 31}{space 2} .0018958{col 42}{space 1}   -4.44{col 51}{space 3}0.000{col 59}{space 4}-.0121266{col 72}{space 3}-.0046952
{txt}{space 14}age {c |}{col 19}{res}{space 2} -.078914{col 31}{space 2} .4200882{col 42}{space 1}   -0.19{col 51}{space 3}0.851{col 59}{space 4}-.9022717{col 72}{space 3} .7444436
{txt}{space 11}female {c |}{col 19}{res}{space 2} 1.371055{col 31}{space 2}   12.002{col 42}{space 1}    0.11{col 51}{space 3}0.909{col 59}{space 4}-22.15242{col 72}{space 3} 24.89453
{txt}{space 4}ep_leadership {c |}{col 19}{res}{space 2} 18.59277{col 31}{space 2} 24.78117{col 42}{space 1}    0.75{col 51}{space 3}0.453{col 59}{space 4}-29.97743{col 72}{space 3} 67.16298
{txt}{space 11}vchair {c |}{col 19}{res}{space 2}-17.52802{col 31}{space 2} 9.241486{col 42}{space 1}   -1.90{col 51}{space 3}0.058{col 59}{space 4}  -35.641{col 72}{space 3} .5849641
{txt}{space 10}epg_no2 {c |}{col 19}{res}{space 2} 20.10035{col 31}{space 2} 25.84377{col 42}{space 1}    0.78{col 51}{space 3}0.437{col 59}{space 4}-30.55251{col 72}{space 3}  70.7532
{txt}{space 10}epg_no3 {c |}{col 19}{res}{space 2} 4.214645{col 31}{space 2} 29.37084{col 42}{space 1}    0.14{col 51}{space 3}0.886{col 59}{space 4}-53.35114{col 72}{space 3} 61.78043
{txt}{space 10}epg_no4 {c |}{col 19}{res}{space 2}-6.490774{col 31}{space 2}   28.487{col 42}{space 1}   -0.23{col 51}{space 3}0.820{col 59}{space 4}-62.32426{col 72}{space 3} 49.34271
{txt}{space 10}epg_no5 {c |}{col 19}{res}{space 2}  52.1601{col 31}{space 2} 31.50919{col 42}{space 1}    1.66{col 51}{space 3}0.098{col 59}{space 4}-9.596775{col 72}{space 3}  113.917
{txt}{space 10}epg_no6 {c |}{col 19}{res}{space 2} 36.33449{col 31}{space 2} 22.70409{col 42}{space 1}    1.60{col 51}{space 3}0.110{col 59}{space 4}-8.164701{col 72}{space 3} 80.83369
{txt}{space 10}epg_no7 {c |}{col 19}{res}{space 2} 168.2996{col 31}{space 2}  48.9438{col 42}{space 1}    3.44{col 51}{space 3}0.001{col 59}{space 4}  72.3715{col 72}{space 3} 264.2277
{txt}{space 10}epg_no8 {c |}{col 19}{res}{space 2} 41.97644{col 31}{space 2} 26.46441{col 42}{space 1}    1.59{col 51}{space 3}0.113{col 59}{space 4}-9.892854{col 72}{space 3} 93.84574
{txt}{space 10}epg_no9 {c |}{col 19}{res}{space 2}-46.23394{col 31}{space 2} 44.82034{col 42}{space 1}   -1.03{col 51}{space 3}0.302{col 59}{space 4}-134.0802{col 72}{space 3} 41.61232
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 84.89179{col 31}{space 2} 123.2717{col 42}{space 1}    0.69{col 51}{space 3}0.491{col 59}{space 4}-156.7162{col 72}{space 3} 326.4998
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}ms_code{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 22.61683{col 44} 9.173276{col 58} 10.21382{col 70} 50.08122
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 103.5669{col 44} 14.48466{col 58} 78.73608{col 70} 136.2286
{txt}{hline 29}{c BT}{hline 48}

{com}. outreg2 using twitter3_1.xls, append label ctitle("Model V","T+1m") onecol dec(3) 
{txt}{browse `"twitter3_1.xls"'}
{browse `"C:\Replication"' :dir}{com} : {txt}{stata `"seeout using "twitter3_1.txt", label"':seeout}

{com}. *Table manually edited*
. 
. ***Figure 1***
. use "C:\Replication\R&P replication data2.dta", clear
{txt}
{com}. rename tminus1w_combined twitter_lastweek
{res}{txt}
{com}. rename citizens_rep tthcitizenMEP 
{res}{txt}
{com}. label variable tthcitizenMEP "H1. Citizens Represented"
{txt}
{com}. rename pref_vote EP_prefvote 
{res}{txt}
{com}. label variable EP_prefvote "H2. Preferential Vote"
{txt}
{com}. rename avg_dm avg_dm2 
{res}{txt}
{com}. label variable avg_dm2 "H2. Avg. District Magnitude"
{txt}
{com}. rename pref_dm pref_dm2
{res}{txt}
{com}. label variable pref_dm2 "H2. Pref. Vote X Avg. DM"
{txt}
{com}. rename May_safety list_safety_may 
{res}{txt}
{com}. label variable list_safety_may "H3. List Safety"
{txt}
{com}. rename seatshare natl_parl_partyseatshare 
{res}{txt}
{com}. label variable natl_parl_partyseatshare "H4. National Party Seat Share"
{txt}
{com}. rename government natl_gov 
{res}{txt}
{com}. label variable natl_gov "H4. National Party in Gov't"
{txt}
{com}. 
. xtmixed twitter_lastweek tthcitizenMEP EP_prefvote avg_dm2 pref_dm2 list_safety_may natl_parl_partyseatshare natl_gov ms_social_network lrgen_w_x galtan_w_x eu_position_w_x followers_tminus1w_com ep_seniority age female ep_leadership vchair epg_no2 epg_no3 epg_no4 epg_no5 epg_no6 epg_no7 epg_no8 epg_no9 || ms_code:, robust cluster(ms_code)
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2084.6121}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-2084.6121}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}       351
{txt}Group variable: {res}ms_code{col 49}{txt}Number of groups{col 67}={col 69}{res}        28

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      12.5
{txt}{col 63}max{col 67}={col 69}{res}        42

{col 49}{txt}Wald chi2({res}25{txt}){col 67}={col 70}{res}  1532.87
{txt}Log pseudolikelihood = {res}-2084.6121{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 90:(Std. Err. adjusted for {res:28} clusters in ms_code)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}        twitter_lastweek{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}tthcitizenMEP {c |}{col 26}{res}{space 2} 1.318242{col 38}{space 2}  .643663{col 49}{space 1}    2.05{col 58}{space 3}0.041{col 66}{space 4} .0566855{col 79}{space 3} 2.579798
{txt}{space 13}EP_prefvote {c |}{col 26}{res}{space 2}-7.592124{col 38}{space 2} 40.25207{col 49}{space 1}   -0.19{col 58}{space 3}0.850{col 66}{space 4}-86.48473{col 79}{space 3} 71.30048
{txt}{space 17}avg_dm2 {c |}{col 26}{res}{space 2}-.8497984{col 38}{space 2} .5435341{col 49}{space 1}   -1.56{col 58}{space 3}0.118{col 66}{space 4}-1.915106{col 79}{space 3} .2155089
{txt}{space 16}pref_dm2 {c |}{col 26}{res}{space 2}-.9947809{col 38}{space 2} 1.552526{col 49}{space 1}   -0.64{col 58}{space 3}0.522{col 66}{space 4}-4.037675{col 79}{space 3} 2.048114
{txt}{space 9}list_safety_may {c |}{col 26}{res}{space 2}-7.294881{col 38}{space 2} 5.125423{col 49}{space 1}   -1.42{col 58}{space 3}0.155{col 66}{space 4}-17.34052{col 79}{space 3} 2.750763
{txt}natl_parl_partyseatshare {c |}{col 26}{res}{space 2}-10.61426{col 38}{space 2} 42.96644{col 49}{space 1}   -0.25{col 58}{space 3}0.805{col 66}{space 4}-94.82693{col 79}{space 3} 73.59841
{txt}{space 16}natl_gov {c |}{col 26}{res}{space 2}-18.90804{col 38}{space 2} 12.20819{col 49}{space 1}   -1.55{col 58}{space 3}0.121{col 66}{space 4}-42.83566{col 79}{space 3} 5.019578
{txt}{space 7}ms_social_network {c |}{col 26}{res}{space 2} .6945467{col 38}{space 2} 1.188998{col 49}{space 1}    0.58{col 58}{space 3}0.559{col 66}{space 4}-1.635847{col 79}{space 3}  3.02494
{txt}{space 15}lrgen_w_x {c |}{col 26}{res}{space 2} 2.892812{col 38}{space 2} 1.512414{col 49}{space 1}    1.91{col 58}{space 3}0.056{col 66}{space 4}-.0714647{col 79}{space 3} 5.857089
{txt}{space 14}galtan_w_x {c |}{col 26}{res}{space 2} .2922758{col 38}{space 2} 1.389732{col 49}{space 1}    0.21{col 58}{space 3}0.833{col 66}{space 4}-2.431549{col 79}{space 3}   3.0161
{txt}{space 9}eu_position_w_x {c |}{col 26}{res}{space 2}-2.551331{col 38}{space 2} 1.859882{col 49}{space 1}   -1.37{col 58}{space 3}0.170{col 66}{space 4}-6.196632{col 79}{space 3}  1.09397
{txt}{space 2}followers_tminus1w_com {c |}{col 26}{res}{space 2} 6.39e-06{col 38}{space 2} .0000726{col 49}{space 1}    0.09{col 58}{space 3}0.930{col 66}{space 4}-.0001359{col 79}{space 3} .0001486
{txt}{space 12}ep_seniority {c |}{col 26}{res}{space 2}-.0055074{col 38}{space 2} .0018381{col 49}{space 1}   -3.00{col 58}{space 3}0.003{col 66}{space 4}-.0091101{col 79}{space 3}-.0019048
{txt}{space 21}age {c |}{col 26}{res}{space 2}-.0938902{col 38}{space 2} .3546361{col 49}{space 1}   -0.26{col 58}{space 3}0.791{col 66}{space 4}-.7889641{col 79}{space 3} .6011838
{txt}{space 18}female {c |}{col 26}{res}{space 2} 9.058423{col 38}{space 2} 9.902359{col 49}{space 1}    0.91{col 58}{space 3}0.360{col 66}{space 4}-10.34984{col 79}{space 3} 28.46669
{txt}{space 11}ep_leadership {c |}{col 26}{res}{space 2} 29.93983{col 38}{space 2} 21.72245{col 49}{space 1}    1.38{col 58}{space 3}0.168{col 66}{space 4}-12.63539{col 79}{space 3} 72.51506
{txt}{space 18}vchair {c |}{col 26}{res}{space 2}-11.70737{col 38}{space 2} 9.153401{col 49}{space 1}   -1.28{col 58}{space 3}0.201{col 66}{space 4}-29.64771{col 79}{space 3} 6.232962
{txt}{space 17}epg_no2 {c |}{col 26}{res}{space 2} 39.41324{col 38}{space 2} 26.03408{col 49}{space 1}    1.51{col 58}{space 3}0.130{col 66}{space 4}-11.61262{col 79}{space 3} 90.43911
{txt}{space 17}epg_no3 {c |}{col 26}{res}{space 2}-28.25896{col 38}{space 2}  38.2191{col 49}{space 1}   -0.74{col 58}{space 3}0.460{col 66}{space 4} -103.167{col 79}{space 3} 46.64911
{txt}{space 17}epg_no4 {c |}{col 26}{res}{space 2}-20.10088{col 38}{space 2} 27.62364{col 49}{space 1}   -0.73{col 58}{space 3}0.467{col 66}{space 4}-74.24222{col 79}{space 3} 34.04046
{txt}{space 17}epg_no5 {c |}{col 26}{res}{space 2} 63.44764{col 38}{space 2} 32.69985{col 49}{space 1}    1.94{col 58}{space 3}0.052{col 66}{space 4}-.6428913{col 79}{space 3} 127.5382
{txt}{space 17}epg_no6 {c |}{col 26}{res}{space 2} 37.36906{col 38}{space 2} 21.46504{col 49}{space 1}    1.74{col 58}{space 3}0.082{col 66}{space 4}-4.701654{col 79}{space 3} 79.43978
{txt}{space 17}epg_no7 {c |}{col 26}{res}{space 2} 144.8232{col 38}{space 2} 36.39209{col 49}{space 1}    3.98{col 58}{space 3}0.000{col 66}{space 4} 73.49599{col 79}{space 3} 216.1503
{txt}{space 17}epg_no8 {c |}{col 26}{res}{space 2} 52.24823{col 38}{space 2} 25.51584{col 49}{space 1}    2.05{col 58}{space 3}0.041{col 66}{space 4} 2.238097{col 79}{space 3} 102.2584
{txt}{space 17}epg_no9 {c |}{col 26}{res}{space 2}-63.74652{col 38}{space 2} 54.10638{col 49}{space 1}   -1.18{col 58}{space 3}0.239{col 66}{space 4}-169.7931{col 79}{space 3} 42.30004
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-31.42353{col 38}{space 2} 101.4907{col 49}{space 1}   -0.31{col 58}{space 3}0.757{col 66}{space 4}-230.3417{col 79}{space 3} 167.4946
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}ms_code{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 26.27413{col 44} 7.644033{col 58} 14.85542{col 70} 46.46989
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 89.55199{col 44} 12.31544{col 58} 68.39362{col 70}  117.256
{txt}{hline 29}{c BT}{hline 48}

{com}. estimates store F
{txt}
{com}. 
. use "C:\Replication\eup-15-1082-File005.dta", clear
{txt}
{com}. label variable tthcitizenMEP "H1. Citizens Represented"
{txt}
{com}. label variable EP_prefvote "H2. Preferential Vote"
{txt}
{com}. label variable avg_dm2 "H2. Avg. District Magnitude"
{txt}
{com}. label variable pref_dm2 "H2. Pref. Vote X Avg. DM"
{txt}
{com}. label variable list_safety_may "H3. List Safety"
{txt}
{com}. label variable natl_parl_partyseatshare "H4. National Party Seat Share"
{txt}
{com}. label variable natl_gov "H4. National Party in Gov't"
{txt}
{com}. 
. xtmixed twitter_lastweek tthcitizenMEP EP_prefvote avg_dm2 pref_dm2 list_safety_may natl_parl_partyseatshare natl_gov mean_age RegSocialMedia internet_user ext_lr ext_galtan ext_integ followers_may terms_served age female EP_leader leadership EPP PES ALDE Verts GUE_NGL EFD ECR || country_code:, robust cluster(country_code)
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1550.5388}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-1549.8701}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-1549.6858}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-1549.6843}  
{res}{txt}Iteration 4:{space 3}log pseudolikelihood = {res:-1549.6843}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}       245
{txt}Group variable: {res}country_code{col 49}{txt}Number of groups{col 67}={col 69}{res}        28

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         1
{txt}{col 63}avg{col 67}={col 69}{res}       8.8
{txt}{col 63}max{col 67}={col 69}{res}        39

{col 49}{txt}Wald chi2({res}26{txt}){col 67}={col 70}{res}134149.68
{txt}Log pseudolikelihood = {res}-1549.6843{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 90:(Std. Err. adjusted for {res:28} clusters in country_code)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}        twitter_lastweek{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}tthcitizenMEP {c |}{col 26}{res}{space 2} 2.662684{col 38}{space 2} .4114364{col 49}{space 1}    6.47{col 58}{space 3}0.000{col 66}{space 4} 1.856283{col 79}{space 3} 3.469084
{txt}{space 13}EP_prefvote {c |}{col 26}{res}{space 2}-49.84234{col 38}{space 2} 22.20732{col 49}{space 1}   -2.24{col 58}{space 3}0.025{col 66}{space 4}-93.36789{col 79}{space 3}-6.316794
{txt}{space 17}avg_dm2 {c |}{col 26}{res}{space 2}-1.351599{col 38}{space 2} .3938622{col 49}{space 1}   -3.43{col 58}{space 3}0.001{col 66}{space 4}-2.123555{col 79}{space 3}-.5796432
{txt}{space 16}pref_dm2 {c |}{col 26}{res}{space 2} 3.238949{col 38}{space 2} 1.334513{col 49}{space 1}    2.43{col 58}{space 3}0.015{col 66}{space 4} .6233513{col 79}{space 3} 5.854546
{txt}{space 9}list_safety_may {c |}{col 26}{res}{space 2}-3.620345{col 38}{space 2} 7.114168{col 49}{space 1}   -0.51{col 58}{space 3}0.611{col 66}{space 4}-17.56386{col 79}{space 3} 10.32317
{txt}natl_parl_partyseatshare {c |}{col 26}{res}{space 2}-.2286893{col 38}{space 2} .5564904{col 49}{space 1}   -0.41{col 58}{space 3}0.681{col 66}{space 4} -1.31939{col 79}{space 3} .8620119
{txt}{space 16}natl_gov {c |}{col 26}{res}{space 2}-14.28625{col 38}{space 2} 16.42076{col 49}{space 1}   -0.87{col 58}{space 3}0.384{col 66}{space 4}-46.47034{col 79}{space 3} 17.89784
{txt}{space 16}mean_age {c |}{col 26}{res}{space 2}-2.118665{col 38}{space 2} 1.391955{col 49}{space 1}   -1.52{col 58}{space 3}0.128{col 66}{space 4}-4.846846{col 79}{space 3} .6095168
{txt}{space 10}RegSocialMedia {c |}{col 26}{res}{space 2} 3.638372{col 38}{space 2} .8462756{col 49}{space 1}    4.30{col 58}{space 3}0.000{col 66}{space 4} 1.979702{col 79}{space 3} 5.297042
{txt}{space 11}internet_user {c |}{col 26}{res}{space 2}-146.3631{col 38}{space 2} 138.9033{col 49}{space 1}   -1.05{col 58}{space 3}0.292{col 66}{space 4}-418.6085{col 79}{space 3} 125.8823
{txt}{space 18}ext_lr {c |}{col 26}{res}{space 2}-7.103247{col 38}{space 2} 3.236849{col 49}{space 1}   -2.19{col 58}{space 3}0.028{col 66}{space 4}-13.44735{col 79}{space 3}-.7591392
{txt}{space 14}ext_galtan {c |}{col 26}{res}{space 2} 5.322451{col 38}{space 2} 1.906357{col 49}{space 1}    2.79{col 58}{space 3}0.005{col 66}{space 4} 1.586058{col 79}{space 3} 9.058843
{txt}{space 15}ext_integ {c |}{col 26}{res}{space 2} .6189773{col 38}{space 2} 3.625767{col 49}{space 1}    0.17{col 58}{space 3}0.864{col 66}{space 4}-6.487396{col 79}{space 3}  7.72535
{txt}{space 11}followers_may {c |}{col 26}{res}{space 2} .0132441{col 38}{space 2} .0058892{col 49}{space 1}    2.25{col 58}{space 3}0.025{col 66}{space 4} .0017015{col 79}{space 3} .0247868
{txt}{space 12}terms_served {c |}{col 26}{res}{space 2}-10.61716{col 38}{space 2} 6.599724{col 49}{space 1}   -1.61{col 58}{space 3}0.108{col 66}{space 4}-23.55238{col 79}{space 3} 2.318057
{txt}{space 21}age {c |}{col 26}{res}{space 2}-2.818799{col 38}{space 2} .8247592{col 49}{space 1}   -3.42{col 58}{space 3}0.001{col 66}{space 4}-4.435297{col 79}{space 3}  -1.2023
{txt}{space 18}female {c |}{col 26}{res}{space 2}  12.0045{col 38}{space 2} 18.88374{col 49}{space 1}    0.64{col 58}{space 3}0.525{col 66}{space 4}-25.00696{col 79}{space 3} 49.01595
{txt}{space 15}EP_leader {c |}{col 26}{res}{space 2}-8.798023{col 38}{space 2}  17.3821{col 49}{space 1}   -0.51{col 58}{space 3}0.613{col 66}{space 4}-42.86632{col 79}{space 3} 25.27027
{txt}{space 14}leadership {c |}{col 26}{res}{space 2}-22.86981{col 38}{space 2} 22.93089{col 49}{space 1}   -1.00{col 58}{space 3}0.319{col 66}{space 4}-67.81353{col 79}{space 3}  22.0739
{txt}{space 21}EPP {c |}{col 26}{res}{space 2} 17.87566{col 38}{space 2} 42.39207{col 49}{space 1}    0.42{col 58}{space 3}0.673{col 66}{space 4}-65.21128{col 79}{space 3} 100.9626
{txt}{space 21}PES {c |}{col 26}{res}{space 2} 39.11568{col 38}{space 2} 42.91665{col 49}{space 1}    0.91{col 58}{space 3}0.362{col 66}{space 4}-44.99941{col 79}{space 3} 123.2308
{txt}{space 20}ALDE {c |}{col 26}{res}{space 2} 36.92297{col 38}{space 2} 46.53834{col 49}{space 1}    0.79{col 58}{space 3}0.428{col 66}{space 4} -54.2905{col 79}{space 3} 128.1364
{txt}{space 19}Verts {c |}{col 26}{res}{space 2}-2.856097{col 38}{space 2} 45.68545{col 49}{space 1}   -0.06{col 58}{space 3}0.950{col 66}{space 4}-92.39792{col 79}{space 3} 86.68573
{txt}{space 17}GUE_NGL {c |}{col 26}{res}{space 2} 50.18199{col 38}{space 2} 44.86779{col 49}{space 1}    1.12{col 58}{space 3}0.263{col 66}{space 4}-37.75727{col 79}{space 3} 138.1213
{txt}{space 21}EFD {c |}{col 26}{res}{space 2}-178.8773{col 38}{space 2} 32.99213{col 49}{space 1}   -5.42{col 58}{space 3}0.000{col 66}{space 4}-243.5407{col 79}{space 3}-114.2139
{txt}{space 21}ECR {c |}{col 26}{res}{space 2} .2656796{col 38}{space 2} 39.10421{col 49}{space 1}    0.01{col 58}{space 3}0.995{col 66}{space 4}-76.37716{col 79}{space 3} 76.90852
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 181.3804{col 38}{space 2} 192.1313{col 49}{space 1}    0.94{col 58}{space 3}0.345{col 66}{space 4}  -195.19{col 79}{space 3} 557.9509
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}country_code{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 2.23e-09{col 44} 1.08e-07{col 58} 9.67e-51{col 70} 5.15e+32
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 135.1389{col 44} 21.38144{col 58} 99.10715{col 70} 184.2706
{txt}{hline 29}{c BT}{hline 48}

{com}. estimates store D
{txt}
{com}. 
. coefplot D, bylabel(2014 Election) ///
>        || F, bylabel(2019 Election)  ///
>        ||, keep(tthcitizenMEP EP_prefvote avg_dm2 pref_dm2 list_safety_may natl_parl_partyseatshare natl_gov) xline(0)
{res}{txt}
{com}. *Figure manually edited in Graph editor*
. 
. ***Table 2***
. use "C:\Replication\R&P replication data2.dta", clear
{txt}
{com}. drop if epg_no2==1
{txt}(34 observations deleted)

{com}. drop if epg_no3==1
{txt}(28 observations deleted)

{com}. drop if epg_no9==1
{txt}(15 observations deleted)

{com}. xtmelogit ownlead_tm2m_bi citizens_rep pref_vote avg_dm pref_dm March_safety seatshare government ms_social_network lrgen_w_x galtan_w_x eu_position_w_x followers_tminus2m ep_seniority age female ep_leadership vchair epg_no4 epg_no5 epg_no6 epg_no7 epg_no8 || ms_code:
{res}
{txt}Refining starting values: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-145.97362}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-144.23939}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-143.43472}  
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-143.43472}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-142.78686}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-142.75413}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-142.75393}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res:-142.75393}  
{res}
{txt}Mixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}       279
{txt}Group variable: {res}ms_code{col 49}{txt}Number of groups{col 67}={col 70}{res}       27

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      10.3
{txt}{col 63}max{col 67}={col 69}{res}        39

{txt}Integration points = {res}  7{col 49}{txt}Wald chi2({res}22{txt}){col 67}={col 70}{res}    34.62
{txt}Log likelihood = {res}-142.75393{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0425

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   ownlead_tm2m_bi{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}citizens_rep {c |}{col 20}{res}{space 2}-.0249451{col 32}{space 2}  .013805{col 43}{space 1}   -1.81{col 52}{space 3}0.071{col 60}{space 4}-.0520025{col 73}{space 3} .0021122
{txt}{space 9}pref_vote {c |}{col 20}{res}{space 2} 1.367303{col 32}{space 2} 1.385855{col 43}{space 1}    0.99{col 52}{space 3}0.324{col 60}{space 4}-1.348923{col 73}{space 3}  4.08353
{txt}{space 12}avg_dm {c |}{col 20}{res}{space 2} .0511143{col 32}{space 2} .0261998{col 43}{space 1}    1.95{col 52}{space 3}0.051{col 60}{space 4}-.0002363{col 73}{space 3} .1024649
{txt}{space 11}pref_dm {c |}{col 20}{res}{space 2}-.0176399{col 32}{space 2} .0425646{col 43}{space 1}   -0.41{col 52}{space 3}0.679{col 60}{space 4}-.1010649{col 73}{space 3} .0657851
{txt}{space 6}March_safety {c |}{col 20}{res}{space 2} .0464709{col 32}{space 2} .1474382{col 43}{space 1}    0.32{col 52}{space 3}0.753{col 60}{space 4}-.2425027{col 73}{space 3} .3354444
{txt}{space 9}seatshare {c |}{col 20}{res}{space 2}-1.190984{col 32}{space 2} 1.486378{col 43}{space 1}   -0.80{col 52}{space 3}0.423{col 60}{space 4}-4.104232{col 73}{space 3} 1.722264
{txt}{space 8}government {c |}{col 20}{res}{space 2} .0335219{col 32}{space 2} .4197291{col 43}{space 1}    0.08{col 52}{space 3}0.936{col 60}{space 4}-.7891321{col 73}{space 3} .8561758
{txt}{space 1}ms_social_network {c |}{col 20}{res}{space 2}  .018988{col 32}{space 2} .0395195{col 43}{space 1}    0.48{col 52}{space 3}0.631{col 60}{space 4}-.0584688{col 73}{space 3} .0964449
{txt}{space 9}lrgen_w_x {c |}{col 20}{res}{space 2}-.0189365{col 32}{space 2}  .066538{col 43}{space 1}   -0.28{col 52}{space 3}0.776{col 60}{space 4}-.1493486{col 73}{space 3} .1114755
{txt}{space 8}galtan_w_x {c |}{col 20}{res}{space 2} .0021627{col 32}{space 2} .0474772{col 43}{space 1}    0.05{col 52}{space 3}0.964{col 60}{space 4}-.0908909{col 73}{space 3} .0952164
{txt}{space 3}eu_position_w_x {c |}{col 20}{res}{space 2} .0562587{col 32}{space 2}   .07496{col 43}{space 1}    0.75{col 52}{space 3}0.453{col 60}{space 4}-.0906603{col 73}{space 3} .2031776
{txt}followers_tminus2m {c |}{col 20}{res}{space 2} 7.67e-06{col 32}{space 2} 4.32e-06{col 43}{space 1}    1.78{col 52}{space 3}0.076{col 60}{space 4}-7.98e-07{col 73}{space 3} .0000161
{txt}{space 6}ep_seniority {c |}{col 20}{res}{space 2} .0002013{col 32}{space 2} .0001009{col 43}{space 1}    2.00{col 52}{space 3}0.046{col 60}{space 4} 3.59e-06{col 73}{space 3} .0003991
{txt}{space 15}age {c |}{col 20}{res}{space 2}-.0064587{col 32}{space 2} .0176819{col 43}{space 1}   -0.37{col 52}{space 3}0.715{col 60}{space 4}-.0411146{col 73}{space 3} .0281972
{txt}{space 12}female {c |}{col 20}{res}{space 2}-.3444554{col 32}{space 2} .3287485{col 43}{space 1}   -1.05{col 52}{space 3}0.295{col 60}{space 4}-.9887905{col 73}{space 3} .2998798
{txt}{space 5}ep_leadership {c |}{col 20}{res}{space 2} .7876807{col 32}{space 2} .7811741{col 43}{space 1}    1.01{col 52}{space 3}0.313{col 60}{space 4}-.7433925{col 73}{space 3} 2.318754
{txt}{space 12}vchair {c |}{col 20}{res}{space 2}-.3929392{col 32}{space 2} .4407697{col 43}{space 1}   -0.89{col 52}{space 3}0.373{col 60}{space 4}-1.256832{col 73}{space 3} .4709536
{txt}{space 11}epg_no4 {c |}{col 20}{res}{space 2} 1.212038{col 32}{space 2} 1.368429{col 43}{space 1}    0.89{col 52}{space 3}0.376{col 60}{space 4}-1.470034{col 73}{space 3}  3.89411
{txt}{space 11}epg_no5 {c |}{col 20}{res}{space 2} 1.599605{col 32}{space 2} 1.288384{col 43}{space 1}    1.24{col 52}{space 3}0.214{col 60}{space 4}-.9255811{col 73}{space 3} 4.124792
{txt}{space 11}epg_no6 {c |}{col 20}{res}{space 2}  2.50274{col 32}{space 2} 1.177102{col 43}{space 1}    2.13{col 52}{space 3}0.033{col 60}{space 4} .1956619{col 73}{space 3} 4.809818
{txt}{space 11}epg_no7 {c |}{col 20}{res}{space 2} 3.435269{col 32}{space 2} 1.327719{col 43}{space 1}    2.59{col 52}{space 3}0.010{col 60}{space 4} .8329869{col 73}{space 3}  6.03755
{txt}{space 11}epg_no8 {c |}{col 20}{res}{space 2} 2.133706{col 32}{space 2} 1.220245{col 43}{space 1}    1.75{col 52}{space 3}0.080{col 60}{space 4}  -.25793{col 73}{space 3} 4.525342
{txt}{space 13}_cons {c |}{col 20}{res}{space 2}-5.158493{col 32}{space 2} 3.442794{col 43}{space 1}   -1.50{col 52}{space 3}0.134{col 60}{space 4}-11.90625{col 73}{space 3}  1.58926
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}ms_code{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33}  .550802{col 44} .2452091{col 58} .2301722{col 70} 1.318069
{txt}{hline 29}{c BT}{hline 48}
LR test vs. logistic model:{col 29}{help j_chibar##|_new:chibar2(01) =} {res}2.70{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0501
{txt}
{com}. outreg2 using twitter3_2.xls, label ctitle("Model I","T-2m") onecol dec(3) replace
{txt}{browse `"twitter3_2.xls"'}
{browse `"C:\Replication"' :dir}{com} : {txt}{stata `"seeout using "twitter3_2.txt", label"':seeout}

{com}. xtmelogit ownlead_tm1m_bi citizens_rep pref_vote avg_dm pref_dm April_safety seatshare government ms_social_network lrgen_w_x galtan_w_x eu_position_w_x followers_tminus1m ep_seniority age female ep_leadership vchair epg_no4 epg_no5 epg_no6 epg_no7 epg_no8 || ms_code:
{res}
{txt}Refining starting values: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-156.68711}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-153.87052}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-153.15585}  
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-153.15585}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-153.14024}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-153.14023}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-153.14023}  
{res}
{txt}Mixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}       270
{txt}Group variable: {res}ms_code{col 49}{txt}Number of groups{col 67}={col 70}{res}       27

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      10.0
{txt}{col 63}max{col 67}={col 69}{res}        35

{txt}Integration points = {res}  7{col 49}{txt}Wald chi2({res}22{txt}){col 67}={col 70}{res}    31.34
{txt}Log likelihood = {res}-153.14023{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0894

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   ownlead_tm1m_bi{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}citizens_rep {c |}{col 20}{res}{space 2}-.0073415{col 32}{space 2} .0117033{col 43}{space 1}   -0.63{col 52}{space 3}0.530{col 60}{space 4}-.0302795{col 73}{space 3} .0155965
{txt}{space 9}pref_vote {c |}{col 20}{res}{space 2} .4026122{col 32}{space 2} 1.339761{col 43}{space 1}    0.30{col 52}{space 3}0.764{col 60}{space 4} -2.22327{col 73}{space 3} 3.028495
{txt}{space 12}avg_dm {c |}{col 20}{res}{space 2} .0263293{col 32}{space 2} .0242177{col 43}{space 1}    1.09{col 52}{space 3}0.277{col 60}{space 4}-.0211365{col 73}{space 3}  .073795
{txt}{space 11}pref_dm {c |}{col 20}{res}{space 2} .0298056{col 32}{space 2} .0390879{col 43}{space 1}    0.76{col 52}{space 3}0.446{col 60}{space 4}-.0468052{col 73}{space 3} .1064165
{txt}{space 6}April_safety {c |}{col 20}{res}{space 2}-.0393618{col 32}{space 2} .1424981{col 43}{space 1}   -0.28{col 52}{space 3}0.782{col 60}{space 4}-.3186529{col 73}{space 3} .2399294
{txt}{space 9}seatshare {c |}{col 20}{res}{space 2}-1.585246{col 32}{space 2} 1.399228{col 43}{space 1}   -1.13{col 52}{space 3}0.257{col 60}{space 4}-4.327682{col 73}{space 3} 1.157191
{txt}{space 8}government {c |}{col 20}{res}{space 2} .5043544{col 32}{space 2} .4021774{col 43}{space 1}    1.25{col 52}{space 3}0.210{col 60}{space 4}-.2838989{col 73}{space 3} 1.292608
{txt}{space 1}ms_social_network {c |}{col 20}{res}{space 2}-.0250972{col 32}{space 2} .0352885{col 43}{space 1}   -0.71{col 52}{space 3}0.477{col 60}{space 4}-.0942615{col 73}{space 3} .0440671
{txt}{space 9}lrgen_w_x {c |}{col 20}{res}{space 2}-.0795837{col 32}{space 2} .0597847{col 43}{space 1}   -1.33{col 52}{space 3}0.183{col 60}{space 4}-.1967595{col 73}{space 3} .0375921
{txt}{space 8}galtan_w_x {c |}{col 20}{res}{space 2} .0210484{col 32}{space 2} .0432565{col 43}{space 1}    0.49{col 52}{space 3}0.627{col 60}{space 4}-.0637327{col 73}{space 3} .1058296
{txt}{space 3}eu_position_w_x {c |}{col 20}{res}{space 2} .1530013{col 32}{space 2} .0682154{col 43}{space 1}    2.24{col 52}{space 3}0.025{col 60}{space 4} .0193016{col 73}{space 3} .2867009
{txt}followers_tminus1m {c |}{col 20}{res}{space 2} 3.03e-06{col 32}{space 2} 3.63e-06{col 43}{space 1}    0.83{col 52}{space 3}0.404{col 60}{space 4}-4.09e-06{col 73}{space 3} .0000102
{txt}{space 6}ep_seniority {c |}{col 20}{res}{space 2} .0001838{col 32}{space 2} .0001029{col 43}{space 1}    1.79{col 52}{space 3}0.074{col 60}{space 4}-.0000178{col 73}{space 3} .0003854
{txt}{space 15}age {c |}{col 20}{res}{space 2}-.0235254{col 32}{space 2} .0166657{col 43}{space 1}   -1.41{col 52}{space 3}0.158{col 60}{space 4}-.0561896{col 73}{space 3} .0091388
{txt}{space 12}female {c |}{col 20}{res}{space 2} .2563911{col 32}{space 2} .3115545{col 43}{space 1}    0.82{col 52}{space 3}0.411{col 60}{space 4}-.3542446{col 73}{space 3} .8670267
{txt}{space 5}ep_leadership {c |}{col 20}{res}{space 2}-.3598091{col 32}{space 2} .8049058{col 43}{space 1}   -0.45{col 52}{space 3}0.655{col 60}{space 4}-1.937396{col 73}{space 3} 1.217777
{txt}{space 12}vchair {c |}{col 20}{res}{space 2} .4638102{col 32}{space 2}  .398574{col 43}{space 1}    1.16{col 52}{space 3}0.245{col 60}{space 4}-.3173805{col 73}{space 3} 1.245001
{txt}{space 11}epg_no4 {c |}{col 20}{res}{space 2} 1.357655{col 32}{space 2} 1.035397{col 43}{space 1}    1.31{col 52}{space 3}0.190{col 60}{space 4}-.6716863{col 73}{space 3} 3.386996
{txt}{space 11}epg_no5 {c |}{col 20}{res}{space 2} 1.175696{col 32}{space 2} .9979142{col 43}{space 1}    1.18{col 52}{space 3}0.239{col 60}{space 4}-.7801795{col 73}{space 3} 3.131572
{txt}{space 11}epg_no6 {c |}{col 20}{res}{space 2} 1.191549{col 32}{space 2} .9231604{col 43}{space 1}    1.29{col 52}{space 3}0.197{col 60}{space 4} -.617812{col 73}{space 3}  3.00091
{txt}{space 11}epg_no7 {c |}{col 20}{res}{space 2} 1.973917{col 32}{space 2}  1.05815{col 43}{space 1}    1.87{col 52}{space 3}0.062{col 60}{space 4}-.1000197{col 73}{space 3} 4.047853
{txt}{space 11}epg_no8 {c |}{col 20}{res}{space 2} .7163568{col 32}{space 2} .9609377{col 43}{space 1}    0.75{col 52}{space 3}0.456{col 60}{space 4}-1.167046{col 73}{space 3}  2.59976
{txt}{space 13}_cons {c |}{col 20}{res}{space 2}-.6554867{col 32}{space 2} 3.107408{col 43}{space 1}   -0.21{col 52}{space 3}0.833{col 60}{space 4}-6.745895{col 73}{space 3} 5.434922
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}ms_code{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} .4225475{col 44} .2550782{col 58} .1294293{col 70} 1.379489
{txt}{hline 29}{c BT}{hline 48}
LR test vs. logistic model:{col 29}{help j_chibar##|_new:chibar2(01) =} {res}1.21{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.1356
{txt}
{com}. outreg2 using twitter3_2.xls, append label ctitle("Model II","T-1m") onecol dec(3) 
{txt}{browse `"twitter3_2.xls"'}
{browse `"C:\Replication"' :dir}{com} : {txt}{stata `"seeout using "twitter3_2.txt", label"':seeout}

{com}. xtmelogit ownlead_tm1w_com_bi citizens_rep pref_vote avg_dm pref_dm May_safety seatshare government ms_social_network lrgen_w_x galtan_w_x eu_position_w_x followers_tminus1w_com ep_seniority age female ep_leadership vchair epg_no4 epg_no5 epg_no6 epg_no7 epg_no8 || ms_code:
{res}
{txt}Refining starting values: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-136.26505}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-135.12125}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-133.73747}  
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-133.73747}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-133.47977}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-133.47396}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-133.47395}  
{res}
{txt}Mixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}       292
{txt}Group variable: {res}ms_code{col 49}{txt}Number of groups{col 67}={col 70}{res}       28

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         1
{txt}{col 63}avg{col 67}={col 69}{res}      10.4
{txt}{col 63}max{col 67}={col 69}{res}        33

{txt}Integration points = {res}  7{col 49}{txt}Wald chi2({res}22{txt}){col 67}={col 70}{res}    29.05
{txt}Log likelihood = {res}-133.47395{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.1436

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   ownlead_tm1w_com_bi{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}citizens_rep {c |}{col 24}{res}{space 2} .0161065{col 36}{space 2} .0193147{col 47}{space 1}    0.83{col 56}{space 3}0.404{col 64}{space 4}-.0217496{col 77}{space 3} .0539627
{txt}{space 13}pref_vote {c |}{col 24}{res}{space 2}-1.752076{col 36}{space 2} 1.550251{col 47}{space 1}   -1.13{col 56}{space 3}0.258{col 64}{space 4}-4.790513{col 77}{space 3} 1.286361
{txt}{space 16}avg_dm {c |}{col 24}{res}{space 2}-.0215345{col 36}{space 2}  .024264{col 47}{space 1}   -0.89{col 56}{space 3}0.375{col 64}{space 4}-.0690912{col 77}{space 3} .0260221
{txt}{space 15}pref_dm {c |}{col 24}{res}{space 2} .1143388{col 36}{space 2} .0602422{col 47}{space 1}    1.90{col 56}{space 3}0.058{col 64}{space 4}-.0037337{col 77}{space 3} .2324113
{txt}{space 12}May_safety {c |}{col 24}{res}{space 2}-.2031037{col 36}{space 2} .1635371{col 47}{space 1}   -1.24{col 56}{space 3}0.214{col 64}{space 4}-.5236305{col 77}{space 3} .1174232
{txt}{space 13}seatshare {c |}{col 24}{res}{space 2}-1.986611{col 36}{space 2}    1.696{col 47}{space 1}   -1.17{col 56}{space 3}0.241{col 64}{space 4}-5.310709{col 77}{space 3} 1.337487
{txt}{space 12}government {c |}{col 24}{res}{space 2}-.0917272{col 36}{space 2} .5005561{col 47}{space 1}   -0.18{col 56}{space 3}0.855{col 64}{space 4}-1.072799{col 77}{space 3} .8893448
{txt}{space 5}ms_social_network {c |}{col 24}{res}{space 2}-.0995448{col 36}{space 2} .0583705{col 47}{space 1}   -1.71{col 56}{space 3}0.088{col 64}{space 4}-.2139488{col 77}{space 3} .0148592
{txt}{space 13}lrgen_w_x {c |}{col 24}{res}{space 2}-.0757539{col 36}{space 2} .0731755{col 47}{space 1}   -1.04{col 56}{space 3}0.301{col 64}{space 4}-.2191752{col 77}{space 3} .0676674
{txt}{space 12}galtan_w_x {c |}{col 24}{res}{space 2}  .112567{col 36}{space 2} .0539047{col 47}{space 1}    2.09{col 56}{space 3}0.037{col 64}{space 4} .0069158{col 77}{space 3} .2182182
{txt}{space 7}eu_position_w_x {c |}{col 24}{res}{space 2} .0575118{col 36}{space 2} .0809969{col 47}{space 1}    0.71{col 56}{space 3}0.478{col 64}{space 4}-.1012393{col 77}{space 3} .2162628
{txt}followers_tminus1w_com {c |}{col 24}{res}{space 2} 7.55e-06{col 36}{space 2} 4.13e-06{col 47}{space 1}    1.83{col 56}{space 3}0.067{col 64}{space 4}-5.43e-07{col 77}{space 3} .0000156
{txt}{space 10}ep_seniority {c |}{col 24}{res}{space 2} .0001261{col 36}{space 2} .0001013{col 47}{space 1}    1.25{col 56}{space 3}0.213{col 64}{space 4}-.0000723{col 77}{space 3} .0003246
{txt}{space 19}age {c |}{col 24}{res}{space 2}-.0278983{col 36}{space 2}     .019{col 47}{space 1}   -1.47{col 56}{space 3}0.142{col 64}{space 4}-.0651377{col 77}{space 3}  .009341
{txt}{space 16}female {c |}{col 24}{res}{space 2} .4240029{col 36}{space 2} .3557516{col 47}{space 1}    1.19{col 56}{space 3}0.233{col 64}{space 4}-.2732574{col 77}{space 3} 1.121263
{txt}{space 9}ep_leadership {c |}{col 24}{res}{space 2} .2414251{col 36}{space 2} .9030342{col 47}{space 1}    0.27{col 56}{space 3}0.789{col 64}{space 4}-1.528489{col 77}{space 3}  2.01134
{txt}{space 16}vchair {c |}{col 24}{res}{space 2} .6324369{col 36}{space 2} .4627465{col 47}{space 1}    1.37{col 56}{space 3}0.172{col 64}{space 4}-.2745296{col 77}{space 3} 1.539403
{txt}{space 15}epg_no4 {c |}{col 24}{res}{space 2} 1.339442{col 36}{space 2} 1.307331{col 47}{space 1}    1.02{col 56}{space 3}0.306{col 64}{space 4} -1.22288{col 77}{space 3} 3.901765
{txt}{space 15}epg_no5 {c |}{col 24}{res}{space 2} 2.488133{col 36}{space 2}   1.3332{col 47}{space 1}    1.87{col 56}{space 3}0.062{col 64}{space 4} -.124891{col 77}{space 3} 5.101157
{txt}{space 15}epg_no6 {c |}{col 24}{res}{space 2} 1.757446{col 36}{space 2} 1.241688{col 47}{space 1}    1.42{col 56}{space 3}0.157{col 64}{space 4} -.676218{col 77}{space 3}  4.19111
{txt}{space 15}epg_no7 {c |}{col 24}{res}{space 2}  1.77591{col 36}{space 2} 1.335219{col 47}{space 1}    1.33{col 56}{space 3}0.184{col 64}{space 4}-.8410706{col 77}{space 3}  4.39289
{txt}{space 15}epg_no8 {c |}{col 24}{res}{space 2} 1.454696{col 36}{space 2} 1.276446{col 47}{space 1}    1.14{col 56}{space 3}0.254{col 64}{space 4}-1.047091{col 77}{space 3} 3.956484
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} 3.486086{col 36}{space 2} 4.522334{col 47}{space 1}    0.77{col 56}{space 3}0.441{col 64}{space 4}-5.377525{col 77}{space 3}  12.3497
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}ms_code{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} .9558455{col 44} .3636696{col 58} .4534526{col 70} 2.014854
{txt}{hline 29}{c BT}{hline 48}
LR test vs. logistic model:{col 29}{help j_chibar##|_new:chibar2(01) =} {res}5.84{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0078
{txt}
{com}. outreg2 using twitter3_2.xls, append label ctitle("Model III","T-1w") onecol dec(3) 
{txt}{browse `"twitter3_2.xls"'}
{browse `"C:\Replication"' :dir}{com} : {txt}{stata `"seeout using "twitter3_2.txt", label"':seeout}

{com}. xtmelogit ownlead_tp1m_bi citizens_rep pref_vote avg_dm pref_dm May_safety seatshare government ms_social_network lrgen_w_x galtan_w_x eu_position_w_x followers_tplus1m ep_seniority age female ep_leadership vchair epg_no4 epg_no5 epg_no6 epg_no7 epg_no8 || ms_code:
{res}
{txt}Refining starting values: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: -142.9106}  (not concave)
{res}{txt}Iteration 1:{space 3}log likelihood = {res: -139.2834}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-138.87451}  
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-138.87451}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-138.87144}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-138.87143}  
{res}
{txt}Mixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}       287
{txt}Group variable: {res}ms_code{col 49}{txt}Number of groups{col 67}={col 70}{res}       28

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         1
{txt}{col 63}avg{col 67}={col 69}{res}      10.3
{txt}{col 63}max{col 67}={col 69}{res}        36

{txt}Integration points = {res}  7{col 49}{txt}Wald chi2({res}22{txt}){col 67}={col 70}{res}    38.03
{txt}Log likelihood = {res}-138.87143{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0182

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  ownlead_tp1m_bi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}citizens_rep {c |}{col 19}{res}{space 2}-.0100883{col 31}{space 2} .0132624{col 42}{space 1}   -0.76{col 51}{space 3}0.447{col 59}{space 4}-.0360822{col 72}{space 3} .0159055
{txt}{space 8}pref_vote {c |}{col 19}{res}{space 2}-1.862352{col 31}{space 2} 1.083672{col 42}{space 1}   -1.72{col 51}{space 3}0.086{col 59}{space 4} -3.98631{col 72}{space 3}  .261605
{txt}{space 11}avg_dm {c |}{col 19}{res}{space 2} .0071685{col 31}{space 2} .0169996{col 42}{space 1}    0.42{col 51}{space 3}0.673{col 59}{space 4}-.0261501{col 72}{space 3}  .040487
{txt}{space 10}pref_dm {c |}{col 19}{res}{space 2} .0371129{col 31}{space 2} .0410017{col 42}{space 1}    0.91{col 51}{space 3}0.365{col 59}{space 4} -.043249{col 72}{space 3} .1174748
{txt}{space 7}May_safety {c |}{col 19}{res}{space 2} .1834379{col 31}{space 2} .1475154{col 42}{space 1}    1.24{col 51}{space 3}0.214{col 59}{space 4} -.105687{col 72}{space 3} .4725627
{txt}{space 8}seatshare {c |}{col 19}{res}{space 2}-2.920727{col 31}{space 2} 1.534844{col 42}{space 1}   -1.90{col 51}{space 3}0.057{col 59}{space 4}-5.928966{col 72}{space 3} .0875122
{txt}{space 7}government {c |}{col 19}{res}{space 2}-.3774997{col 31}{space 2} .4417026{col 42}{space 1}   -0.85{col 51}{space 3}0.393{col 59}{space 4}-1.243221{col 72}{space 3} .4882216
{txt}ms_social_network {c |}{col 19}{res}{space 2} .0365328{col 31}{space 2} .0414272{col 42}{space 1}    0.88{col 51}{space 3}0.378{col 59}{space 4} -.044663{col 72}{space 3} .1177285
{txt}{space 8}lrgen_w_x {c |}{col 19}{res}{space 2}-.0573089{col 31}{space 2} .0716553{col 42}{space 1}   -0.80{col 51}{space 3}0.424{col 59}{space 4}-.1977507{col 72}{space 3} .0831329
{txt}{space 7}galtan_w_x {c |}{col 19}{res}{space 2} .0193059{col 31}{space 2} .0472949{col 42}{space 1}    0.41{col 51}{space 3}0.683{col 59}{space 4}-.0733904{col 72}{space 3} .1120022
{txt}{space 2}eu_position_w_x {c |}{col 19}{res}{space 2} .0035135{col 31}{space 2} .0771987{col 42}{space 1}    0.05{col 51}{space 3}0.964{col 59}{space 4}-.1477931{col 72}{space 3} .1548201
{txt}followers_tplus1m {c |}{col 19}{res}{space 2} 7.65e-07{col 31}{space 2} 3.86e-06{col 42}{space 1}    0.20{col 51}{space 3}0.843{col 59}{space 4}-6.79e-06{col 72}{space 3} 8.32e-06
{txt}{space 5}ep_seniority {c |}{col 19}{res}{space 2} .0000193{col 31}{space 2} .0000986{col 42}{space 1}    0.20{col 51}{space 3}0.845{col 59}{space 4}-.0001739{col 72}{space 3} .0002126
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0183623{col 31}{space 2} .0184979{col 42}{space 1}   -0.99{col 51}{space 3}0.321{col 59}{space 4}-.0546176{col 72}{space 3}  .017893
{txt}{space 11}female {c |}{col 19}{res}{space 2}-.0612774{col 31}{space 2} .3331272{col 42}{space 1}   -0.18{col 51}{space 3}0.854{col 59}{space 4}-.7141946{col 72}{space 3} .5916399
{txt}{space 4}ep_leadership {c |}{col 19}{res}{space 2}  .387806{col 31}{space 2} .7719667{col 42}{space 1}    0.50{col 51}{space 3}0.615{col 59}{space 4}-1.125221{col 72}{space 3} 1.900833
{txt}{space 11}vchair {c |}{col 19}{res}{space 2}-.3318269{col 31}{space 2} .4400523{col 42}{space 1}   -0.75{col 51}{space 3}0.451{col 59}{space 4}-1.194313{col 72}{space 3} .5306597
{txt}{space 10}epg_no4 {c |}{col 19}{res}{space 2} 13.55369{col 31}{space 2} 730.5103{col 42}{space 1}    0.02{col 51}{space 3}0.985{col 59}{space 4} -1418.22{col 72}{space 3} 1445.328
{txt}{space 10}epg_no5 {c |}{col 19}{res}{space 2} 15.47583{col 31}{space 2}  730.509{col 42}{space 1}    0.02{col 51}{space 3}0.983{col 59}{space 4}-1416.295{col 72}{space 3} 1447.247
{txt}{space 10}epg_no6 {c |}{col 19}{res}{space 2} 16.98651{col 31}{space 2}  730.509{col 42}{space 1}    0.02{col 51}{space 3}0.981{col 59}{space 4}-1414.785{col 72}{space 3} 1448.758
{txt}{space 10}epg_no7 {c |}{col 19}{res}{space 2} 16.02674{col 31}{space 2} 730.5088{col 42}{space 1}    0.02{col 51}{space 3}0.982{col 59}{space 4}-1415.744{col 72}{space 3} 1447.798
{txt}{space 10}epg_no8 {c |}{col 19}{res}{space 2} 15.12398{col 31}{space 2} 730.5089{col 42}{space 1}    0.02{col 51}{space 3}0.983{col 59}{space 4}-1416.647{col 72}{space 3} 1446.895
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-16.03615{col 31}{space 2}  730.516{col 42}{space 1}   -0.02{col 51}{space 3}0.982{col 59}{space 4}-1447.821{col 72}{space 3} 1415.749
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}ms_code{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33}  .538281{col 44} .2339745{col 58} .2296241{col 70} 1.261829
{txt}{hline 29}{c BT}{hline 48}
LR test vs. logistic model:{col 29}{help j_chibar##|_new:chibar2(01) =} {res}2.83{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0462
{txt}
{com}. outreg2 using twitter3_2.xls, append label ctitle("Model IV","T+1m") onecol dec(3) 
{txt}{browse `"twitter3_2.xls"'}
{browse `"C:\Replication"' :dir}{com} : {txt}{stata `"seeout using "twitter3_2.txt", label"':seeout}

{com}. *Table manually edited*
. 
. ***Figure 2***
. use "C:\Replication\R&P replication data2.dta", clear
{txt}
{com}. drop if epg_no2==1
{txt}(34 observations deleted)

{com}. drop if epg_no3==1
{txt}(28 observations deleted)

{com}. drop if epg_no9==1
{txt}(15 observations deleted)

{com}. xtmelogit ownlead_tm1w_com_bi citizens_rep i.pref_vote##c.avg_dm May_safety seatshare government ms_social_network lrgen_w_x galtan_w_x eu_position_w_x followers_tminus1w_com ep_seniority age female ep_leadership vchair epg_no4 epg_no5 epg_no6 epg_no7 epg_no8 || ms_code:
{res}
{txt}Refining starting values: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-136.26505}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-135.12125}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-133.73747}  
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-133.73747}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-133.47977}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-133.47396}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-133.47395}  
{res}
{txt}Mixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}       292
{txt}Group variable: {res}ms_code{col 49}{txt}Number of groups{col 67}={col 70}{res}       28

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         1
{txt}{col 63}avg{col 67}={col 69}{res}      10.4
{txt}{col 63}max{col 67}={col 69}{res}        33

{txt}Integration points = {res}  7{col 49}{txt}Wald chi2({res}22{txt}){col 67}={col 70}{res}    29.05
{txt}Log likelihood = {res}-133.47395{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.1436

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   ownlead_tm1w_com_bi{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}citizens_rep {c |}{col 24}{res}{space 2} .0161065{col 36}{space 2} .0193147{col 47}{space 1}    0.83{col 56}{space 3}0.404{col 64}{space 4}-.0217496{col 77}{space 3} .0539627
{txt}{space 11}1.pref_vote {c |}{col 24}{res}{space 2}-1.752076{col 36}{space 2} 1.550251{col 47}{space 1}   -1.13{col 56}{space 3}0.258{col 64}{space 4}-4.790513{col 77}{space 3} 1.286361
{txt}{space 16}avg_dm {c |}{col 24}{res}{space 2}-.0215345{col 36}{space 2}  .024264{col 47}{space 1}   -0.89{col 56}{space 3}0.375{col 64}{space 4}-.0690912{col 77}{space 3} .0260221
{txt}{space 22} {c |}
{space 4}pref_vote#c.avg_dm {c |}
{space 20}1  {c |}{col 24}{res}{space 2} .1143388{col 36}{space 2} .0602422{col 47}{space 1}    1.90{col 56}{space 3}0.058{col 64}{space 4}-.0037337{col 77}{space 3} .2324113
{txt}{space 22} {c |}
{space 12}May_safety {c |}{col 24}{res}{space 2}-.2031037{col 36}{space 2} .1635371{col 47}{space 1}   -1.24{col 56}{space 3}0.214{col 64}{space 4}-.5236305{col 77}{space 3} .1174232
{txt}{space 13}seatshare {c |}{col 24}{res}{space 2}-1.986611{col 36}{space 2}    1.696{col 47}{space 1}   -1.17{col 56}{space 3}0.241{col 64}{space 4}-5.310709{col 77}{space 3} 1.337487
{txt}{space 12}government {c |}{col 24}{res}{space 2}-.0917272{col 36}{space 2} .5005561{col 47}{space 1}   -0.18{col 56}{space 3}0.855{col 64}{space 4}-1.072799{col 77}{space 3} .8893448
{txt}{space 5}ms_social_network {c |}{col 24}{res}{space 2}-.0995448{col 36}{space 2} .0583705{col 47}{space 1}   -1.71{col 56}{space 3}0.088{col 64}{space 4}-.2139488{col 77}{space 3} .0148592
{txt}{space 13}lrgen_w_x {c |}{col 24}{res}{space 2}-.0757539{col 36}{space 2} .0731755{col 47}{space 1}   -1.04{col 56}{space 3}0.301{col 64}{space 4}-.2191752{col 77}{space 3} .0676674
{txt}{space 12}galtan_w_x {c |}{col 24}{res}{space 2}  .112567{col 36}{space 2} .0539047{col 47}{space 1}    2.09{col 56}{space 3}0.037{col 64}{space 4} .0069158{col 77}{space 3} .2182182
{txt}{space 7}eu_position_w_x {c |}{col 24}{res}{space 2} .0575118{col 36}{space 2} .0809969{col 47}{space 1}    0.71{col 56}{space 3}0.478{col 64}{space 4}-.1012393{col 77}{space 3} .2162628
{txt}followers_tminus1w_com {c |}{col 24}{res}{space 2} 7.55e-06{col 36}{space 2} 4.13e-06{col 47}{space 1}    1.83{col 56}{space 3}0.067{col 64}{space 4}-5.43e-07{col 77}{space 3} .0000156
{txt}{space 10}ep_seniority {c |}{col 24}{res}{space 2} .0001261{col 36}{space 2} .0001013{col 47}{space 1}    1.25{col 56}{space 3}0.213{col 64}{space 4}-.0000723{col 77}{space 3} .0003246
{txt}{space 19}age {c |}{col 24}{res}{space 2}-.0278983{col 36}{space 2}     .019{col 47}{space 1}   -1.47{col 56}{space 3}0.142{col 64}{space 4}-.0651377{col 77}{space 3}  .009341
{txt}{space 16}female {c |}{col 24}{res}{space 2} .4240029{col 36}{space 2} .3557516{col 47}{space 1}    1.19{col 56}{space 3}0.233{col 64}{space 4}-.2732574{col 77}{space 3} 1.121263
{txt}{space 9}ep_leadership {c |}{col 24}{res}{space 2} .2414251{col 36}{space 2} .9030342{col 47}{space 1}    0.27{col 56}{space 3}0.789{col 64}{space 4}-1.528489{col 77}{space 3}  2.01134
{txt}{space 16}vchair {c |}{col 24}{res}{space 2} .6324369{col 36}{space 2} .4627465{col 47}{space 1}    1.37{col 56}{space 3}0.172{col 64}{space 4}-.2745296{col 77}{space 3} 1.539403
{txt}{space 15}epg_no4 {c |}{col 24}{res}{space 2} 1.339442{col 36}{space 2} 1.307331{col 47}{space 1}    1.02{col 56}{space 3}0.306{col 64}{space 4} -1.22288{col 77}{space 3} 3.901765
{txt}{space 15}epg_no5 {c |}{col 24}{res}{space 2} 2.488133{col 36}{space 2}   1.3332{col 47}{space 1}    1.87{col 56}{space 3}0.062{col 64}{space 4} -.124891{col 77}{space 3} 5.101157
{txt}{space 15}epg_no6 {c |}{col 24}{res}{space 2} 1.757446{col 36}{space 2} 1.241688{col 47}{space 1}    1.42{col 56}{space 3}0.157{col 64}{space 4} -.676218{col 77}{space 3}  4.19111
{txt}{space 15}epg_no7 {c |}{col 24}{res}{space 2}  1.77591{col 36}{space 2} 1.335219{col 47}{space 1}    1.33{col 56}{space 3}0.184{col 64}{space 4}-.8410706{col 77}{space 3}  4.39289
{txt}{space 15}epg_no8 {c |}{col 24}{res}{space 2} 1.454696{col 36}{space 2} 1.276446{col 47}{space 1}    1.14{col 56}{space 3}0.254{col 64}{space 4}-1.047091{col 77}{space 3} 3.956484
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} 3.486086{col 36}{space 2} 4.522334{col 47}{space 1}    0.77{col 56}{space 3}0.441{col 64}{space 4}-5.377525{col 77}{space 3}  12.3497
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}ms_code{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} .9558455{col 44} .3636696{col 58} .4534526{col 70} 2.014854
{txt}{hline 29}{c BT}{hline 48}
LR test vs. logistic model:{col 29}{help j_chibar##|_new:chibar2(01) =} {res}5.84{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0078
{txt}
{com}. margins, at(avg_dm=(3 (5) 74) pref_vote=(0 1)) predict(mu fixedonly) atmeans
{res}
{txt}Adjusted predictions{col 49}Number of obs{col 67}= {res}       292

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted mean, fixed portion only, predict(mu fixedonly)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:citizens_rep}{space 4}{txt:=} {space 3}67.24143 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:pref_vote}{space 7}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:avg_dm}{space 10}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:May_safety}{space 6}{txt:=} {space 8}1.5 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:seatshare}{space 7}{txt:=} {space 3}.2108365 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:government}{space 6}{txt:=} {space 3}.3732877 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:ms_social_~k}{space 4}{txt:=} {space 3}60.87506 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lrgen_w_x}{space 7}{txt:=} {space 3}4.014251 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:galtan_w_x}{space 6}{txt:=} {space 3}4.947351 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:eu_positio~x}{space 4}{txt:=} {space 4}4.02556 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:followers~om}{space 4}{txt:=} {space 3}17103.36 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:ep_seniority}{space 4}{txt:=} {space 3}2927.271 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:age}{space 13}{txt:=} {space 3}54.04986 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:female}{space 10}{txt:=} {space 4}.380137 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:ep_leaders~p}{space 4}{txt:=} {space 3}.0410959 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:vchair}{space 10}{txt:=} {space 3}.1541096 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:epg_no4}{space 9}{txt:=} {space 4}.109589 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:epg_no5}{space 9}{txt:=} {space 3}.0856164 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:epg_no6}{space 9}{txt:=} {space 3}.3424658 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:epg_no7}{space 9}{txt:=} {space 3}.0890411 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:epg_no8}{space 9}{txt:=} {space 3}.2910959 {txt:(mean)}}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:citizens_rep}{space 4}{txt:=} {space 3}67.24143 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:pref_vote}{space 7}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:avg_dm}{space 10}{txt:=} {space 10}8}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:May_safety}{space 6}{txt:=} {space 8}1.5 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:seatshare}{space 7}{txt:=} {space 3}.2108365 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:government}{space 6}{txt:=} {space 3}.3732877 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:ms_social_~k}{space 4}{txt:=} {space 3}60.87506 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lrgen_w_x}{space 7}{txt:=} {space 3}4.014251 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:galtan_w_x}{space 6}{txt:=} {space 3}4.947351 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:eu_positio~x}{space 4}{txt:=} {space 4}4.02556 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:followers~om}{space 4}{txt:=} {space 3}17103.36 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:ep_seniority}{space 4}{txt:=} {space 3}2927.271 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:age}{space 13}{txt:=} {space 3}54.04986 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:female}{space 10}{txt:=} {space 4}.380137 {txt:(mean)}}{p_end}
{p2colreset}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:pref_vote}{space 7}{txt:=} {space 10}0}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:avg_dm}{space 10}{txt:=} {space 9}68}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:May_safety}{space 6}{txt:=} {space 8}1.5 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:seatshare}{space 7}{txt:=} {space 3}.2108365 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lrgen_w_x}{space 7}{txt:=} {space 3}4.014251 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:galtan_w_x}{space 6}{txt:=} {space 3}4.947351 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:eu_positio~x}{space 4}{txt:=} {space 4}4.02556 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:followers~om}{space 4}{txt:=} {space 3}17103.36 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:ep_seniority}{space 4}{txt:=} {space 3}2927.271 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:age}{space 13}{txt:=} {space 3}54.04986 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:female}{space 10}{txt:=} {space 4}.380137 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:vchair}{space 10}{txt:=} {space 3}.1541096 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:epg_no8}{space 9}{txt:=} {space 3}.2910959 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:pref_vote}{space 7}{txt:=} {space 10}0}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:avg_dm}{space 10}{txt:=} {space 9}73}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:seatshare}{space 7}{txt:=} {space 3}.2108365 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:government}{space 6}{txt:=} {space 3}.3732877 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lrgen_w_x}{space 7}{txt:=} {space 3}4.014251 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:galtan_w_x}{space 6}{txt:=} {space 3}4.947351 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:eu_positio~x}{space 4}{txt:=} {space 4}4.02556 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:age}{space 13}{txt:=} {space 3}54.04986 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:epg_no8}{space 9}{txt:=} {space 3}.2910959 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:pref_vote}{space 7}{txt:=} {space 10}1}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:government}{space 6}{txt:=} {space 3}.3732877 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lrgen_w_x}{space 7}{txt:=} {space 3}4.014251 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:galtan_w_x}{space 6}{txt:=} {space 3}4.947351 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:age}{space 13}{txt:=} {space 3}54.04986 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:vchair}{space 10}{txt:=} {space 3}.1541096 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:epg_no8}{space 9}{txt:=} {space 3}.2910959 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:pref_vote}{space 7}{txt:=} {space 10}1}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:avg_dm}{space 10}{txt:=} {space 10}8}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:May_safety}{space 6}{txt:=} {space 8}1.5 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:seatshare}{space 7}{txt:=} {space 3}.2108365 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lrgen_w_x}{space 7}{txt:=} {space 3}4.014251 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:galtan_w_x}{space 6}{txt:=} {space 3}4.947351 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:eu_positio~x}{space 4}{txt:=} {space 4}4.02556 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:followers~om}{space 4}{txt:=} {space 3}17103.36 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:age}{space 13}{txt:=} {space 3}54.04986 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:female}{space 10}{txt:=} {space 4}.380137 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:vchair}{space 10}{txt:=} {space 3}.1541096 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:epg_no8}{space 9}{txt:=} {space 3}.2910959 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:pref_vote}{space 7}{txt:=} {space 10}1}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:avg_dm}{space 10}{txt:=} {space 9}13}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:May_safety}{space 6}{txt:=} {space 8}1.5 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:seatshare}{space 7}{txt:=} {space 3}.2108365 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:government}{space 6}{txt:=} {space 3}.3732877 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lrgen_w_x}{space 7}{txt:=} {space 3}4.014251 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:galtan_w_x}{space 6}{txt:=} {space 3}4.947351 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:eu_positio~x}{space 4}{txt:=} {space 4}4.02556 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:followers~om}{space 4}{txt:=} {space 3}17103.36 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:age}{space 13}{txt:=} {space 3}54.04986 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:ep_leaders~p}{space 4}{txt:=} {space 3}.0410959 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:vchair}{space 10}{txt:=} {space 3}.1541096 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:epg_no6}{space 9}{txt:=} {space 3}.3424658 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:epg_no7}{space 9}{txt:=} {space 3}.0890411 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:epg_no8}{space 9}{txt:=} {space 3}.2910959 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:pref_vote}{space 7}{txt:=} {space 10}1}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:avg_dm}{space 10}{txt:=} {space 9}18}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:May_safety}{space 6}{txt:=} {space 8}1.5 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:seatshare}{space 7}{txt:=} {space 3}.2108365 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:government}{space 6}{txt:=} {space 3}.3732877 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:ms_social_~k}{space 4}{txt:=} {space 3}60.87506 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lrgen_w_x}{space 7}{txt:=} {space 3}4.014251 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:galtan_w_x}{space 6}{txt:=} {space 3}4.947351 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:eu_positio~x}{space 4}{txt:=} {space 4}4.02556 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:followers~om}{space 4}{txt:=} {space 3}17103.36 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:ep_seniority}{space 4}{txt:=} {space 3}2927.271 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:age}{space 13}{txt:=} {space 3}54.04986 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:female}{space 10}{txt:=} {space 4}.380137 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .2637873{col 26}{space 2} .2113544{col 37}{space 1}    1.25{col 46}{space 3}0.212{col 54}{space 4}-.1504597{col 67}{space 3} .6780343
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .2434147{col 26}{space 2} .1830163{col 37}{space 1}    1.33{col 46}{space 3}0.184{col 54}{space 4}-.1152907{col 67}{space 3}   .60212
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .2241365{col 26}{space 2} .1574879{col 37}{space 1}    1.42{col 46}{space 3}0.155{col 54}{space 4}-.0845342{col 67}{space 3} .5328072
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .2059694{col 26}{space 2} .1350798{col 37}{space 1}    1.52{col 46}{space 3}0.127{col 54}{space 4}-.0587821{col 67}{space 3}  .470721
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1889163{col 26}{space 2} .1160529{col 37}{space 1}    1.63{col 46}{space 3}0.104{col 54}{space 4}-.0385431{col 67}{space 3} .4163758
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1729676{col 26}{space 2} .1005976{col 37}{space 1}    1.72{col 46}{space 3}0.086{col 54}{space 4}-.0242002{col 67}{space 3} .3701353
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .1581028{col 26}{space 2} .0887804{col 37}{space 1}    1.78{col 46}{space 3}0.075{col 54}{space 4}-.0159035{col 67}{space 3} .3321091
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .1442926{col 26}{space 2} .0804604{col 37}{space 1}    1.79{col 46}{space 3}0.073{col 54}{space 4}-.0134069{col 67}{space 3} .3019922
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .1315003{col 26}{space 2}  .075226{col 37}{space 1}    1.75{col 46}{space 3}0.080{col 54}{space 4}-.0159399{col 67}{space 3} .2789405
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .1196835{col 26}{space 2} .0724225{col 37}{space 1}    1.65{col 46}{space 3}0.098{col 54}{space 4} -.022262{col 67}{space 3}  .261629
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .1087956{col 26}{space 2} .0712884{col 37}{space 1}    1.53{col 46}{space 3}0.127{col 54}{space 4}-.0309272{col 67}{space 3} .2485183
{txt}{space 9}12  {c |}{col 14}{res}{space 2}  .098787{col 26}{space 2} .0711167{col 37}{space 1}    1.39{col 46}{space 3}0.165{col 54}{space 4}-.0405993{col 67}{space 3} .2381732
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .0896065{col 26}{space 2} .0713536{col 37}{space 1}    1.26{col 46}{space 3}0.209{col 54}{space 4} -.050244{col 67}{space 3} .2294571
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .0812024{col 26}{space 2} .0716185{col 37}{space 1}    1.13{col 46}{space 3}0.257{col 54}{space 4}-.0591673{col 67}{space 3}  .221572
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .0735228{col 26}{space 2}  .071678{col 37}{space 1}    1.03{col 46}{space 3}0.305{col 54}{space 4}-.0669636{col 67}{space 3} .2140092
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .0805102{col 26}{space 2} .0624447{col 37}{space 1}    1.29{col 46}{space 3}0.197{col 54}{space 4}-.0418793{col 67}{space 3} .2028996
{txt}{space 9}17  {c |}{col 14}{res}{space 2}  .122237{col 26}{space 2} .0686648{col 37}{space 1}    1.78{col 46}{space 3}0.075{col 54}{space 4}-.0123435{col 67}{space 3} .2568175
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .1813254{col 26}{space 2} .0753787{col 37}{space 1}    2.41{col 46}{space 3}0.016{col 54}{space 4} .0335859{col 67}{space 3}  .329065
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .2604999{col 26}{space 2} .0975477{col 37}{space 1}    2.67{col 46}{space 3}0.008{col 54}{space 4} .0693098{col 67}{space 3} .4516899
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .3590819{col 26}{space 2} .1465089{col 37}{space 1}    2.45{col 46}{space 3}0.014{col 54}{space 4} .0719297{col 67}{space 3} .6462342
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .4711994{col 26}{space 2} .2091233{col 37}{space 1}    2.25{col 46}{space 3}0.024{col 54}{space 4} .0613254{col 67}{space 3} .8810735
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .5863002{col 26}{space 2} .2605365{col 37}{space 1}    2.25{col 46}{space 3}0.024{col 54}{space 4}  .075658{col 67}{space 3} 1.096942
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .6926872{col 26}{space 2} .2818816{col 37}{space 1}    2.46{col 46}{space 3}0.014{col 54}{space 4} .1402094{col 67}{space 3} 1.245165
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .7818932{col 26}{space 2}  .269826{col 37}{space 1}    2.90{col 46}{space 3}0.004{col 54}{space 4}  .253044{col 67}{space 3} 1.310743
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .8507829{col 26}{space 2} .2342126{col 37}{space 1}    3.63{col 46}{space 3}0.000{col 54}{space 4} .3917346{col 67}{space 3} 1.309831
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .9006776{col 26}{space 2} .1887992{col 37}{space 1}    4.77{col 46}{space 3}0.000{col 54}{space 4}  .530638{col 67}{space 3} 1.270717
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .9351601{col 26}{space 2} .1441925{col 37}{space 1}    6.49{col 46}{space 3}0.000{col 54}{space 4} .6525479{col 67}{space 3} 1.217772
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .9582263{col 26}{space 2} .1059513{col 37}{space 1}    9.04{col 46}{space 3}0.000{col 54}{space 4} .7505656{col 67}{space 3} 1.165887
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .9733211{col 26}{space 2} .0757395{col 37}{space 1}   12.85{col 46}{space 3}0.000{col 54}{space 4} .8248744{col 67}{space 3} 1.121768
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .9830578{col 26}{space 2} .0530853{col 37}{space 1}   18.52{col 46}{space 3}0.000{col 54}{space 4} .8790126{col 67}{space 3} 1.087103
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, recast(line) recastci(rarea) xlabels(0 (5) 75) plot1opts(lpattern(dash) lcolor(black)) plot2opts(lcolor(black)) xtitle("Average district magnitude") ytitle("Predicted marginal means") legend(label(1 "Preferential Voting Systems") label(2 "Non-Preferential Voting Systems"))

{text}{p 2 6 2}Variables that uniquely identify margins: avg_dm pref_vote{p_end}
{res}{txt}
{com}. *Figure manually edited in Graph editor*
. 
. ***Figure 3***
. use "C:\Replication\R&P replication data2.dta", clear
{txt}
{com}. keep if epg_no6==1 
{txt}(429 observations deleted)

{com}. gen total_mentions =ownlead_tm2m+ownlead_tm1m
{txt}(19 missing values generated)

{com}. graph bar (mean) total_mentions, over(ms) ytitle("Mean number of mentions") title("EPP: Average of mentions during two-month campaign")
{res}{txt}
{com}. *Figure manually edited in Graph editor*
. 
. ***Figure 4***
. use "C:\Replication\R&P replication data2.dta", clear
{txt}
{com}. keep if epg_no8==1 
{txt}(445 observations deleted)

{com}. gen total_mentions =ownlead_tm2m+ownlead_tm1m
{txt}(18 missing values generated)

{com}. graph bar (mean) total_mentions, over(ms) ytitle("Mean number of mentions") title("S&D: Average of mentions during two-month campaign")
{res}{txt}
{com}. *Figure manually edited in Graph editor*
. 
. ***Appendix A***
. use "C:\Replication\R&P replication data2.dta", clear
{txt}
{com}. drop if ms=="Great Britain"
{txt}(62 observations deleted)

{com}. drop if ms=="Northern Ireland"
{txt}(3 observations deleted)

{com}. xtmixed tminus1w_combined citizens_rep pref_vote avg_dm pref_dm May_safety seatshare government ms_social_network lrgen_w_x galtan_w_x eu_position_w_x followers_tminus1w_com ep_seniority age female ep_leadership vchair epg_no2 epg_no3 epg_no4 epg_no5 epg_no6 epg_no7 epg_no8 epg_no9 || ms_code:, robust cluster(ms_code)
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1823.2458}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-1823.2458}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}       314
{txt}Group variable: {res}ms_code{col 49}{txt}Number of groups{col 67}={col 69}{res}        27

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      11.6
{txt}{col 63}max{col 67}={col 69}{res}        42

{col 49}{txt}Wald chi2({res}25{txt}){col 67}={col 70}{res}   602.48
{txt}Log pseudolikelihood = {res}-1823.2458{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 88:(Std. Err. adjusted for {res:27} clusters in ms_code)}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}     tminus1w_combined{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}citizens_rep {c |}{col 24}{res}{space 2} .8889945{col 36}{space 2} .6414903{col 47}{space 1}    1.39{col 56}{space 3}0.166{col 64}{space 4}-.3683035{col 77}{space 3} 2.146292
{txt}{space 13}pref_vote {c |}{col 24}{res}{space 2}  21.9039{col 36}{space 2}  35.1782{col 47}{space 1}    0.62{col 56}{space 3}0.534{col 64}{space 4} -47.0441{col 77}{space 3} 90.85189
{txt}{space 16}avg_dm {c |}{col 24}{res}{space 2} .1465747{col 36}{space 2} .6125824{col 47}{space 1}    0.24{col 56}{space 3}0.811{col 64}{space 4}-1.054065{col 77}{space 3} 1.347214
{txt}{space 15}pref_dm {c |}{col 24}{res}{space 2}-1.062564{col 36}{space 2} 1.398207{col 47}{space 1}   -0.76{col 56}{space 3}0.447{col 64}{space 4}   -3.803{col 77}{space 3} 1.677872
{txt}{space 12}May_safety {c |}{col 24}{res}{space 2}-12.19676{col 36}{space 2} 5.441443{col 47}{space 1}   -2.24{col 56}{space 3}0.025{col 64}{space 4}-22.86179{col 77}{space 3}-1.531727
{txt}{space 13}seatshare {c |}{col 24}{res}{space 2}-11.20338{col 36}{space 2} 42.61197{col 47}{space 1}   -0.26{col 56}{space 3}0.793{col 64}{space 4} -94.7213{col 77}{space 3} 72.31454
{txt}{space 12}government {c |}{col 24}{res}{space 2}-11.75791{col 36}{space 2}  11.9682{col 47}{space 1}   -0.98{col 56}{space 3}0.326{col 64}{space 4}-35.21515{col 77}{space 3} 11.69933
{txt}{space 5}ms_social_network {c |}{col 24}{res}{space 2} .7371482{col 36}{space 2} 1.039784{col 47}{space 1}    0.71{col 56}{space 3}0.478{col 64}{space 4} -1.30079{col 77}{space 3} 2.775087
{txt}{space 13}lrgen_w_x {c |}{col 24}{res}{space 2} 1.743943{col 36}{space 2} 1.424081{col 47}{space 1}    1.22{col 56}{space 3}0.221{col 64}{space 4}-1.047204{col 77}{space 3} 4.535089
{txt}{space 12}galtan_w_x {c |}{col 24}{res}{space 2}-1.167731{col 36}{space 2} 1.093216{col 47}{space 1}   -1.07{col 56}{space 3}0.285{col 64}{space 4}-3.310395{col 77}{space 3} .9749328
{txt}{space 7}eu_position_w_x {c |}{col 24}{res}{space 2}-1.578075{col 36}{space 2} 1.496929{col 47}{space 1}   -1.05{col 56}{space 3}0.292{col 64}{space 4}-4.512001{col 77}{space 3}  1.35585
{txt}followers_tminus1w_com {c |}{col 24}{res}{space 2} .0002739{col 36}{space 2} .0001252{col 47}{space 1}    2.19{col 56}{space 3}0.029{col 64}{space 4} .0000286{col 77}{space 3} .0005192
{txt}{space 10}ep_seniority {c |}{col 24}{res}{space 2}-.0035411{col 36}{space 2} .0023458{col 47}{space 1}   -1.51{col 56}{space 3}0.131{col 64}{space 4}-.0081387{col 77}{space 3} .0010566
{txt}{space 19}age {c |}{col 24}{res}{space 2}-.1374083{col 36}{space 2} .3750646{col 47}{space 1}   -0.37{col 56}{space 3}0.714{col 64}{space 4}-.8725214{col 77}{space 3} .5977048
{txt}{space 16}female {c |}{col 24}{res}{space 2} 7.879791{col 36}{space 2} 10.27268{col 47}{space 1}    0.77{col 56}{space 3}0.443{col 64}{space 4} -12.2543{col 77}{space 3} 28.01388
{txt}{space 9}ep_leadership {c |}{col 24}{res}{space 2} 29.30474{col 36}{space 2} 21.96207{col 47}{space 1}    1.33{col 56}{space 3}0.182{col 64}{space 4}-13.74012{col 77}{space 3}  72.3496
{txt}{space 16}vchair {c |}{col 24}{res}{space 2}-9.796822{col 36}{space 2} 9.456269{col 47}{space 1}   -1.04{col 56}{space 3}0.300{col 64}{space 4}-28.33077{col 77}{space 3} 8.737124
{txt}{space 15}epg_no2 {c |}{col 24}{res}{space 2} 29.66599{col 36}{space 2} 27.80156{col 47}{space 1}    1.07{col 56}{space 3}0.286{col 64}{space 4}-24.82407{col 77}{space 3} 84.15604
{txt}{space 15}epg_no3 {c |}{col 24}{res}{space 2}-7.388672{col 36}{space 2} 30.15658{col 47}{space 1}   -0.25{col 56}{space 3}0.806{col 64}{space 4}-66.49448{col 77}{space 3} 51.71714
{txt}{space 15}epg_no4 {c |}{col 24}{res}{space 2} 14.74949{col 36}{space 2} 20.43391{col 47}{space 1}    0.72{col 56}{space 3}0.470{col 64}{space 4}-25.30025{col 77}{space 3} 54.79922
{txt}{space 15}epg_no5 {c |}{col 24}{res}{space 2}  45.8126{col 36}{space 2} 31.47211{col 47}{space 1}    1.46{col 56}{space 3}0.145{col 64}{space 4} -15.8716{col 77}{space 3} 107.4968
{txt}{space 15}epg_no6 {c |}{col 24}{res}{space 2}  23.8354{col 36}{space 2} 19.66036{col 47}{space 1}    1.21{col 56}{space 3}0.225{col 64}{space 4}-14.69821{col 77}{space 3}   62.369
{txt}{space 15}epg_no7 {c |}{col 24}{res}{space 2} 125.6457{col 36}{space 2} 40.46534{col 47}{space 1}    3.11{col 56}{space 3}0.002{col 64}{space 4} 46.33513{col 77}{space 3} 204.9563
{txt}{space 15}epg_no8 {c |}{col 24}{res}{space 2} 29.50867{col 36}{space 2}  22.0046{col 47}{space 1}    1.34{col 56}{space 3}0.180{col 64}{space 4}-13.61956{col 77}{space 3} 72.63689
{txt}{space 15}epg_no9 {c |}{col 24}{res}{space 2}-26.32153{col 36}{space 2} 31.72721{col 47}{space 1}   -0.83{col 56}{space 3}0.407{col 64}{space 4}-88.50571{col 77}{space 3} 35.86266
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}-37.83527{col 36}{space 2} 88.29008{col 47}{space 1}   -0.43{col 56}{space 3}0.668{col 64}{space 4}-210.8807{col 77}{space 3} 135.2101
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}ms_code{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 27.63157{col 44} 5.923026{col 58} 18.15283{col 70} 42.05975
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 77.79502{col 44} 10.81645{col 58}  59.2383{col 70} 102.1647
{txt}{hline 29}{c BT}{hline 48}

{com}. outreg2 using twitter3_A.xls, label ctitle("Model IV","T-1 week") onecol dec(3) replace
{txt}{browse `"twitter3_A.xls"'}
{browse `"C:\Replication"' :dir}{com} : {txt}{stata `"seeout using "twitter3_A.txt", label"':seeout}

{com}. xtmixed tplus1m citizens_rep pref_vote avg_dm pref_dm May_safety seatshare government ms_social_network lrgen_w_x galtan_w_x eu_position_w_x followers_tplus1m ep_seniority age female ep_leadership vchair epg_no2 epg_no3 epg_no4 epg_no5 epg_no6 epg_no7 epg_no8 epg_no9 || ms_code:, robust cluster(ms_code)
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1878.7163}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-1878.7152}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-1878.7152}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}       314
{txt}Group variable: {res}ms_code{col 49}{txt}Number of groups{col 67}={col 69}{res}        27

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}      11.6
{txt}{col 63}max{col 67}={col 69}{res}        42

{col 49}{txt}Wald chi2({res}25{txt}){col 67}={col 70}{res}  3351.51
{txt}Log pseudolikelihood = {res}-1878.7152{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 83:(Std. Err. adjusted for {res:27} clusters in ms_code)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}          tplus1m{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}citizens_rep {c |}{col 19}{res}{space 2} .7093305{col 31}{space 2}  .592951{col 42}{space 1}    1.20{col 51}{space 3}0.232{col 59}{space 4}-.4528321{col 72}{space 3} 1.871493
{txt}{space 8}pref_vote {c |}{col 19}{res}{space 2}  36.7108{col 31}{space 2} 37.51814{col 42}{space 1}    0.98{col 51}{space 3}0.328{col 59}{space 4} -36.8234{col 72}{space 3}  110.245
{txt}{space 11}avg_dm {c |}{col 19}{res}{space 2}-.0143938{col 31}{space 2} .7339369{col 42}{space 1}   -0.02{col 51}{space 3}0.984{col 59}{space 4}-1.452884{col 72}{space 3} 1.424096
{txt}{space 10}pref_dm {c |}{col 19}{res}{space 2}-1.659564{col 31}{space 2} 1.405956{col 42}{space 1}   -1.18{col 51}{space 3}0.238{col 59}{space 4}-4.415188{col 72}{space 3}  1.09606
{txt}{space 7}May_safety {c |}{col 19}{res}{space 2}-11.27793{col 31}{space 2}  6.75387{col 42}{space 1}   -1.67{col 51}{space 3}0.095{col 59}{space 4}-24.51528{col 72}{space 3} 1.959407
{txt}{space 8}seatshare {c |}{col 19}{res}{space 2} 31.86248{col 31}{space 2} 48.75345{col 42}{space 1}    0.65{col 51}{space 3}0.513{col 59}{space 4}-63.69253{col 72}{space 3} 127.4175
{txt}{space 7}government {c |}{col 19}{res}{space 2}-3.679189{col 31}{space 2} 11.34883{col 42}{space 1}   -0.32{col 51}{space 3}0.746{col 59}{space 4}-25.92249{col 72}{space 3} 18.56412
{txt}ms_social_network {c |}{col 19}{res}{space 2}-.8240122{col 31}{space 2} 1.085497{col 42}{space 1}   -0.76{col 51}{space 3}0.448{col 59}{space 4}-2.951548{col 72}{space 3} 1.303523
{txt}{space 8}lrgen_w_x {c |}{col 19}{res}{space 2} .8429251{col 31}{space 2} 2.064452{col 42}{space 1}    0.41{col 51}{space 3}0.683{col 59}{space 4}-3.203327{col 72}{space 3} 4.889177
{txt}{space 7}galtan_w_x {c |}{col 19}{res}{space 2}-2.172978{col 31}{space 2} 1.185193{col 42}{space 1}   -1.83{col 51}{space 3}0.067{col 59}{space 4}-4.495914{col 72}{space 3} .1499582
{txt}{space 2}eu_position_w_x {c |}{col 19}{res}{space 2}-2.584609{col 31}{space 2} 2.007498{col 42}{space 1}   -1.29{col 51}{space 3}0.198{col 59}{space 4}-6.519233{col 72}{space 3} 1.350016
{txt}followers_tplus1m {c |}{col 19}{res}{space 2} .0002652{col 31}{space 2} .0001183{col 42}{space 1}    2.24{col 51}{space 3}0.025{col 59}{space 4} .0000334{col 72}{space 3}  .000497
{txt}{space 5}ep_seniority {c |}{col 19}{res}{space 2} -.008124{col 31}{space 2} .0026559{col 42}{space 1}   -3.06{col 51}{space 3}0.002{col 59}{space 4}-.0133295{col 72}{space 3}-.0029186
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.2270118{col 31}{space 2} .4435332{col 42}{space 1}   -0.51{col 51}{space 3}0.609{col 59}{space 4}-1.096321{col 72}{space 3} .6422973
{txt}{space 11}female {c |}{col 19}{res}{space 2}-.7908192{col 31}{space 2} 12.54458{col 42}{space 1}   -0.06{col 51}{space 3}0.950{col 59}{space 4}-25.37775{col 72}{space 3} 23.79611
{txt}{space 4}ep_leadership {c |}{col 19}{res}{space 2} 18.06855{col 31}{space 2} 27.03372{col 42}{space 1}    0.67{col 51}{space 3}0.504{col 59}{space 4}-34.91657{col 72}{space 3} 71.05368
{txt}{space 11}vchair {c |}{col 19}{res}{space 2}-16.99247{col 31}{space 2} 10.25291{col 42}{space 1}   -1.66{col 51}{space 3}0.097{col 59}{space 4} -37.0878{col 72}{space 3} 3.102855
{txt}{space 10}epg_no2 {c |}{col 19}{res}{space 2} 2.992519{col 31}{space 2} 24.23423{col 42}{space 1}    0.12{col 51}{space 3}0.902{col 59}{space 4} -44.5057{col 72}{space 3} 50.49074
{txt}{space 10}epg_no3 {c |}{col 19}{res}{space 2} 21.99085{col 31}{space 2} 25.07642{col 42}{space 1}    0.88{col 51}{space 3}0.381{col 59}{space 4}-27.15803{col 72}{space 3} 71.13973
{txt}{space 10}epg_no4 {c |}{col 19}{res}{space 2} 30.31561{col 31}{space 2} 20.79971{col 42}{space 1}    1.46{col 51}{space 3}0.145{col 59}{space 4}-10.45107{col 72}{space 3} 71.08229
{txt}{space 10}epg_no5 {c |}{col 19}{res}{space 2}  35.1664{col 31}{space 2} 30.98135{col 42}{space 1}    1.14{col 51}{space 3}0.256{col 59}{space 4}-25.55593{col 72}{space 3} 95.88873
{txt}{space 10}epg_no6 {c |}{col 19}{res}{space 2} 25.02484{col 31}{space 2} 24.53378{col 42}{space 1}    1.02{col 51}{space 3}0.308{col 59}{space 4}-23.06048{col 72}{space 3} 73.11016
{txt}{space 10}epg_no7 {c |}{col 19}{res}{space 2} 160.8409{col 31}{space 2} 56.10104{col 42}{space 1}    2.87{col 51}{space 3}0.004{col 59}{space 4}  50.8849{col 72}{space 3} 270.7969
{txt}{space 10}epg_no8 {c |}{col 19}{res}{space 2} 20.02258{col 31}{space 2} 26.27472{col 42}{space 1}    0.76{col 51}{space 3}0.446{col 59}{space 4}-31.47492{col 72}{space 3} 71.52007
{txt}{space 10}epg_no9 {c |}{col 19}{res}{space 2}-20.90494{col 31}{space 2} 26.20706{col 42}{space 1}   -0.80{col 51}{space 3}0.425{col 59}{space 4}-72.26983{col 72}{space 3} 30.45995
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 84.83704{col 31}{space 2} 109.0547{col 42}{space 1}    0.78{col 51}{space 3}0.437{col 59}{space 4}-128.9063{col 72}{space 3} 298.5804
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}ms_code{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 23.45232{col 44}  6.30816{col 58} 13.84304{col 70}   39.732
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 94.02513{col 44} 14.43443{col 58} 69.59375{col 70} 127.0333
{txt}{hline 29}{c BT}{hline 48}

{com}. outreg2 using twitter3_A.xls, append label ctitle("Model V","T+1 month") onecol dec(3) 
{txt}{browse `"twitter3_A.xls"'}
{browse `"C:\Replication"' :dir}{com} : {txt}{stata `"seeout using "twitter3_A.txt", label"':seeout}

{com}. *Table manually edited*
. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Replication\log file.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}18 Dec 2019, 22:42:28
{txt}{.-}
{smcl}
{txt}{sf}{ul off}